[go: up one dir, main page]

WO2018102440A1 - Système et procédé de traitement électronique de transactions de véhicule sur la base d'une détection d'image d'une plaque d'immatriculation de véhicule - Google Patents

Système et procédé de traitement électronique de transactions de véhicule sur la base d'une détection d'image d'une plaque d'immatriculation de véhicule Download PDF

Info

Publication number
WO2018102440A1
WO2018102440A1 PCT/US2017/063755 US2017063755W WO2018102440A1 WO 2018102440 A1 WO2018102440 A1 WO 2018102440A1 US 2017063755 W US2017063755 W US 2017063755W WO 2018102440 A1 WO2018102440 A1 WO 2018102440A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
image
license plate
mobile apparatus
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2017/063755
Other languages
English (en)
Inventor
Anthony Russell WILBERT
David Ching-Chien CHUNG
Hans Brandon Wach
Goran Matko RAUKER
Solomon John WHITE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Blinker Inc
Original Assignee
Blinker Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/363,960 external-priority patent/US9818154B1/en
Application filed by Blinker Inc filed Critical Blinker Inc
Priority to CA3045147A priority Critical patent/CA3045147A1/fr
Publication of WO2018102440A1 publication Critical patent/WO2018102440A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0613—Electronic shopping [e-shopping] using intermediate agents
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/17—Image acquisition using hand-held instruments
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
    • G06V10/95—Hardware or software architectures specially adapted for image or video understanding structured as a network, e.g. client-server architectures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/63—Scene text, e.g. street names
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/247—Aligning, centring, orientation detection or correction of the image by affine transforms, e.g. correction due to perspective effects; Quadrilaterals, e.g. trapezoids
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/625—License plates

Definitions

  • the present disclosure relates generally to a method and apparatus for detecting license plate information from an image of a license plate and more specifically, detecting license plate information from an optical image, captured by a mobile apparatus, that includes a license plate image and several other object images.
  • camera equipped mobile apparatuses e.g., smartphones
  • Mobile apparatuses are frequently used to capture optical images and for many users serve as a replacement for a simple digital camera because the camera equipped mobile apparatus provides an image that is often as good as those produced by simple digital cameras and can easily be transmitted (shared) over a network.
  • the method includes steps for receiving, by a computer processor, vehicle license plate information based on an optical image of a vehicle license plate captured by an image sensor of a first mobile apparatus and converted into an electrical signal that is processed to identify the vehicle license plate information; identifying, by the computer processor, vehicle configuration information based on the vehicle license plate information and transmitting the vehicle configuration information to the first mobile apparatus; posting, by the computer processor, a vehicle sales information on an external website in response to a posting request from the first mobile apparatus, the posting request including at least an offered sales price; receiving, by the computer processor, confirmation from at least one of the first mobile apparatus and a second mobile apparatus that users of the respective mobile apparatuses have agreed to transfer ownership of the vehicle; and in response to the confirmation, facilitating transfer of ownership of the vehicle between the users.
  • the system includes a first mobile apparatus having: an image sensor configured to capture an optical image of a vehicle license plate and convert the optical image into an electrical signal, a license plate detector configured to identify the vehicle license plate information based on the electrical signal, and an interface configured to transmit the vehicle license plate information.
  • the system includes a remote server having a computer processor and configured to: identify vehicle configuration information based on the vehicle license plate information received from the first mobile apparatus, transmit the vehicle configuration information to the first mobile device, post vehicle sales information on an external website in response to a posting request from the first mobile apparatus, the posting request including at least an offered sales price.
  • the system further includes a second mobile apparatus configured to display the vehicle sales information posted on the external website.
  • the remote server is further configured to receive confirmation from at least one of the first mobile apparatus and the second mobile apparatus that users of the respective mobile apparatuses have agreed to transfer ownership of the vehicle and, in response to the confirmation, facilitate a transfer of ownership of the vehicle between the users.
  • FIG. 1 conceptually illustrates an exemplary embodiment of an apparatus that is capable of capturing an optical image and detecting a license plate image from the optical image.
  • FIG. 2 illustrates an exemplary embodiment transmitting license plate information derived from an optical image to an external server.
  • FIG. 3A illustrates an exemplary embodiment of an apparatus for displaying vehicle configuration information and posting a vehicle for sale from a license plate image.
  • FIG. 3B illustrates an exemplary embodiment of vehicle information that has been posted to a website.
  • FIG. 4 conceptually illustrates an exemplary embodiment of a process of receiving vehicle configuration information and posting a vehicle for sale from an optical image.
  • FIGS. 5A and 5B conceptually illustrate an exemplary embodiment of receiving vehicle configuration information and posting a vehicle for sale from a video.
  • FIG. 6 illustrates an exemplary embodiment of a system architecture of a license plate detection apparatus.
  • FIG. 7 illustrates an exemplary embodiment of a diagram of the format converter.
  • FIG. 8 illustrates an exemplary embodiment of a diagram of the image filter.
  • FIG. 9 illustrates an exemplary embodiment of a diagram of a license plate detector.
  • FIG. 10 illustrates an exemplary embodiment of an object image with a convex hull fit around the object image.
  • FIG. 11 illustrates an exemplary embodiment of a method for forming a quadrilateral from a convex hull.
  • FIG. 12 illustrates an exemplary embodiment of an object image enclosed in a quadrilateral.
  • FIG. 13 illustrates an exemplary embodiment of a diagram of the rendering module.
  • FIG. 14 illustrates an exemplary embodiment of a scene that may be captured by a license plate detection apparatus.
  • FIG. 15 provides a high level illustration of an exemplary embodiment of how an image may be rendered on a mobile apparatus by the license plate detection apparatus and transmission of a detected license plate image to a server.
  • FIG. 16 conceptually illustrates an exemplary embodiment of a more detailed process for processing an electrical signal to recover license plate information.
  • FIG. 17 illustrates an exemplary embodiment of an object image comprising a license plate image within a rectangle.
  • FIG. 18 illustrates an exemplary embodiment of an object image comprising a license plate image within a quadrilateral.
  • FIG. 19 is an illustration of an exemplary embodiment of the dewarping process being performed on a license plate image.
  • FIG. 20 conceptually illustrates an exemplary embodiment of a process for processing an optical image comprising a license plate image.
  • FIG. 21 illustrates an exemplary embodiment of a diagram for determining whether a patch is an actual license plate image.
  • FIG. 22 conceptually illustrates an exemplary embodiment of a process for processing a patch comprising a candidate license plate image.
  • FIG. 23 illustrates an exemplary embodiment of an operating environment for communication between a gateway and client apparatuses.
  • FIG. 24 illustrates an exemplary embodiment of data flow between a gateway and various other modules.
  • FIG. 25 conceptually illustrates an exemplary embodiment of a process for receiving vehicle configuration information and posting a vehicle for sale from a license plate image.
  • FIG. 26 conceptually illustrates a flowchart for a method for transmitting confirmation of vehicle ownership from a license plate image.
  • FIG. 27A conceptually illustrates a flowchart for a method for a prospective buyer to view the vehicle information and electronically transmit a purchase offer.
  • FIG. 27B illustrates a user interface of a buyer's mobile apparatus for viewing the vehicle information and electronically transmitting a purchase offer.
  • FIGS. 28A and 28B conceptually illustrate a flowchart for a method for finalizing and executing a vehicle transaction.
  • FIG. 29A conceptually illustrates a flowchart for a method for finalizing a vehicle transaction.
  • FIG. 29B illustrates user interfaces for each of the buyer and seller for finalizing a vehicle transaction.
  • FIG. 29C illustrates a screen shot of a user interface for an administrator for finalizing a vehicle transaction.
  • FIG. 30 illustrates an exemplary embodiment of an electronic system that may implement the license plate detection apparatus.
  • FIGS. 31A-31D illustrate detailed flowcharts for methods for processing a prospective buyer for facilitating a vehicle sale according to an exemplary aspect.
  • FIGS. 32A-32F illustrate screenshots of a user interface for buyer to select financing and payment terms according to an exemplary aspect.
  • FIG. 1 conceptually illustrates an exemplary embodiment of an apparatus 130 that is capable of capturing an optical image and detecting a license plate 120 from the optical image.
  • the apparatus 130 may be a mobile phone, personal digital assistants (PDA), smart phone, laptop computer, palm-sized computer, tablet computer, game console, media player, digital camera, or any other suitable apparatus.
  • FIG. 1 includes a vehicle 1 10, the license plate 120 registered to the vehicle 1 10, the apparatus 130, touch screen 140, and a user 150.
  • the apparatus 130 may be a wireless handheld device with built in image capture capabilities such as the smart phone, tablet or personal data assistant (PDA) described above.
  • the apparatus 130 may be a digital camera capable of processing or transferring data derived from the captured image to a personal computer. The information may then be uploaded from the personal computer to the license plate detection apparatus discussed in the foregoing.
  • a customized application is installed on the apparatus 130.
  • the customized application may interface with the apparatus' image capture device to capture an optical image, convert the optical image to an electrical signal, process the electrical signal to detect the presence of a license plate image, and derive license plate information from a portion of the electrical signal that is associated with the license plate image.
  • the license plate information may be transmitted wirelessly to a server for further processing or decoding such as optical character recognition (OCR) of the license plate image.
  • OCR optical character recognition
  • the OCR process may be carried out on the mobile apparatus 130.
  • the customized software application can downloaded from a remote server (e.g., server 230 discussed below) and/or from an "Apps Store" that has been provided with a license to download the software application to mobile device users.
  • a remote server e.g., server 230 discussed below
  • the customized software application enables each mobile device (of the seller and buyer, for example) to communicate required information to the server 230 to facilitate the transaction of the vehicle, including loan information and the like.
  • the server is capable of receiving such information from each mobile device and performing the processes described herein.
  • the apparatus 130 may receive an interaction from the user 150 to capture an optical image that includes an object image of the license plate 120.
  • the interaction may occur at the touch screen 140.
  • the touch screen 140 shows an exemplary rendering of the optical image, including a rendering of the license plate image that may be captured by the apparatus 130.
  • the image of the license plate 120 may include a number and a state. OCR software may be used to convert the state and number portions of the license plate image to text, which may be stored as strings to be used later for various functions.
  • OCR software may be used to convert the state and number portions of the license plate image to text, which may be stored as strings to be used later for various functions.
  • the server and/or the apparatus application may provide error checking capability to ensure that the captured image is clear enough to accurately detect and decode a license plate image.
  • the apparatus 130 may display an alert in the display area 140, which may guide the user to acquiring a suitable image.
  • some aspects of the apparatus may provide the capability to bypass the image capture process to instead provide a user interface with text fields.
  • the user interface may provide text fields that allow for entry of the license plate number and state.
  • the entered information may be provided as text strings to the license plate detection apparatus without going through the detection process discussed above.
  • FIG. 2 illustrates an exemplary embodiment of transmitting license plate information derived from an optical image to an external server 230.
  • a license plate image may be transmitted after recovering the license plate information from an image 220.
  • FIG. 2 includes an apparatus 210, the image 220, the server 230, and the Internet 240.
  • the apparatus 210 may be a mobile or wireless apparatus.
  • the image 220 may be the same as the image rendered on the display area 140 as illustrated in FIG. 1.
  • a license plate image recovered from the image 220 may be transmitted over the internet
  • server 230 can be configured to identity the vehicle using a partial plate. For example, if only a portion of the alphanumeric characters can be identified, but the state of the license plate is identifiable, the server 230 may be configured to identify the vehicle based on this partial match.
  • the 210 may receive and display a confirmation message for confirming that the derived license plate information (e.g., state and license plate number) is correct.
  • the apparatus 210 may also display information about the vehicle to help the user determine whether the derived license plate information is correct. This may be useful in cases such as when the apparatus 210 captures a license plate image of a moving vehicle. The vehicle license plate may no longer be in eyesight. However, it may be possible to determine with some degree of accuracy whether the derived license plate information is correct based on the vehicle information that is displayed on the mobile apparatus.
  • FIG. 3A illustrates an exemplary embodiment of an apparatus for displaying vehicle configuration information and posting a vehicle for sale from a license plate image.
  • the apparatus 300 may be a handheld wireless device.
  • the apparatus 300 includes a display area 310.
  • the display area 310 includes license plate information 320, selectable user interface (UI) objects 370 and 330, vehicle configuration information 345, and vehicle data 350.
  • FIG. 3A illustrates two stages 301 and 302 of a user's interaction with the apparatus 300.
  • the apparatus 300 may have transmitted a license plate image to the server 230 for further processing. Such processing will be described in the foregoing figures.
  • the apparatus 300 may display the license plate information 320. Additionally, the display area 310 may provide the configuration information 345 about the vehicle to assist the user in further identifying that the recovered license plate information is accurate. Moreover, the configuration information may be used later if the user wishes to post the vehicle for sale at an external website. Once the user has verified that the information in the display area 310 matches up with the vehicle associated with the license plate image and driver's license information, the apparatus 300 may receive a selection of the selectable UI object 370 to confirm the information.
  • the apparatus 300 may have received a selection of the selectable
  • the display area 310 may present the vehicle data 350.
  • the vehicle data 350 may be pre-populated based on the known configuration information or the vehicle data 350 may be received by input from a user.
  • the vehicle data 350 may be editable by user input.
  • the user may wish to adjust some of the configuration information because, for instance, the vehicle may have some aftermarket parts installed that were not part of the vehicle configuration information 345.
  • the information may be edited by selecting the "show vehicle options" object of the vehicle data 350.
  • the apparatus may receive user input of the vehicle mileage, a price, and condition (not shown). Once all of the information has been received at the apparatus, the apparatus may receive a user interaction with the selectable UI object 330 to post the vehicle for sale.
  • FIGS 1-3B Providing the interface described in FIGS 1-3B provides an easy and efficient way for sellers of vehicles to make informed decisions.
  • the interface provides sellers with accurate information so the seller can feel comfortable with determining a fair cost for selling a particular vehicle. Additionally, the information is gathered by simply capturing an image of a vehicle license plate and providing no, or minimal, further interaction.
  • FIG. 3B illustrates an exemplary embodiment of vehicle information that has been posted to a website.
  • FIG 3B includes a web browser 380, vehicle information 390, vehicle image 395, vehicle configuration information and data 397, and scrollable object 385.
  • the web browser 380 may be displayed on a mobile apparatus or a personal computer.
  • the web browser 380 includes many of the features commonly found on traditional web browsers such as buttons and a text box for entering a URL.
  • the web browser 380 in this exemplary illustration is displaying the vehicle described in
  • FIG. 3A after the apparatus 300 received a selection to post the vehicle configuration information to a website.
  • the website may be a website typically used for listing vehicles that are for sale.
  • information such as the vehicle information 390, vehicle image 395, and vehicle configuration and data 397 may be pertinent to a buyer looking to purchase a vehicle.
  • the vehicle image 395 may be a representative image of the vehicle for sale, or it may be an actual image of the vehicle for sale as captured by the apparatus 300.
  • Such images may be included when the apparatus 300 receives the selection to post the vehicle configuration to the website.
  • the apparatus is not limited to only one photo. As most car listing services provide the option to upload several photos, the apparatus 300 may provide several images of the vehicle to use when posting the vehicle configuration to the website.
  • the web browser 380 also includes price information, mileage, and options/features that are included in the vehicle as vehicle configuration information and data 397. Additional information about the vehicle may be displayed lower in the web browser 380 window. In such instances, the scrollable object 385 may be used to scroll up and down based on received user input to view all of the vehicle features.
  • the interface discussed in FIGS. 1-3B provide a seamless mechanism for posting a vehicle for sale without having to manually enter all of the vehicle configuration information.
  • FIG. 4 conceptually illustrates an exemplary embodiment of a process 400 of receiving vehicle configuration information and posting a vehicle for sale from an optical image.
  • the process 400 may be performed by a mobile apparatus such as the apparatus 130 described with respect to FIG. 1.
  • the process 400 may begin after an image capture capability or an application is initiated on the mobile apparatus.
  • the application may enable the image capture feature on the mobile apparatus.
  • the process 400 captures (at 410) an optical image that includes a vehicle license plate image.
  • an optical image that includes a vehicle license plate image.
  • some aspects of the apparatus may process a video. A frame may then be extracted and converted to an image file.
  • the process 400 converts the optical image into an electrical signal.
  • the 400 then processes (at 430) the electrical signal to recover license plate information.
  • the process 400 determines (at 440) whether the license plate information was successfully recovered. When the license plate information was successfully recovered, the process 400 transmits (at 460) the license plate information to a remote server.
  • the process 400 receives (at 470) vehicle configuration information corresponding to the vehicle license plate.
  • the process 400 receives (at 480) user input to post the vehicle configuration information to a website.
  • the process 400 ends.
  • the process 400 displays (at 450) an alert that the license plate information was not recovered.
  • an alert that the license plate information was not recovered.
  • a message guiding the user to position the mobile apparatus to achieve greater chances of recovering the license plate information may be provided with the displayed alert.
  • the process then ends. However, in some aspects of the process, rather than end, the process may optionally return to capture (at 410) another optical image and repeat the entire process 400.
  • FIGS. 5a and 5b conceptually illustrate an exemplary embodiment of a process 500 of receiving vehicle configuration information and posting a vehicle for sale from a video.
  • the process 500 may be performed by a mobile apparatus such as the apparatus 130 described with respect to FIG. 1.
  • the process 500 may begin after an image and/or video capture capability or an application is initiated on the mobile apparatus.
  • the application may enable the image and/or video capture feature on the mobile apparatus.
  • the process 500 converts (at 505) an optical image into an electrical signal for sampling the electrical signal at n frames/second (fps).
  • the process may sample the electrical signal at intervals such as 24 fps or any other suitable interval for capturing video according to the apparatus' capabilities.
  • Each sample of the electrical signal represents a frame of a video image presented on a display.
  • the process 500 samples (at 510) a first portion of the electrical signal representing a first frame of the video image presented on the display.
  • the process determines (at 515) whether any object image(s) are detected within the frame. At least one of the detected object image(s) may comprise a license plate image.
  • the process 500 assigns (at 520) a score based on the detected object image.
  • the score may be based on the likelihood that at least one of the object images is a license plate image and is discussed in greater detail below with respect to FIGS. 21 and 22.
  • the score may be applied to each object image and/or aggregated for each object image detected in the frame.
  • the process may then store (at 525) the score and associated object image and frame information in a data structure.
  • the process 500 may store the aggregated object image score and/or the process 500 may store the highest scoring object image in the frame.
  • the process 500 determines (at 515) that no object image exists within the frame or after the process 500 stores the score (at 525), the process 500 displays feedback to a user based on the object image detected (or not detected). For instance, when no object image is detected in the frame, the process 500 may display a message guiding the user on how to collect a better optical image. However, when at least one object image is detected in the frame, the process 500 may provide feedback by overlaying rectangles around the detected object image(s). Alternatively or conjunctively, the process 500 may overlay a rectangle that provides a visual cue such as a distinct color, indicating which object image is determined to most likely be a license plate image or has a higher score than other object images within the frame. In some aspects, the visual cue may be provided when a particular object image receives a score above a threshold value.
  • a visual cue such as a distinct color
  • the process 500 optionally determines (at 535) whether user input has been received to stop the video. Such user input may include a gestural interaction with the mobile apparatus, which deactivates the camera shutter on the mobile apparatus.
  • the process 500 selects (at 545) the highest scoring frame according to the stored frame information.
  • the process 500 determines (at 540) whether to sample additional portions of the electrical signal. In some aspects of the process, such a determination may be based on a predetermined number of samples.
  • the mobile apparatus may have a built in and/or configurable setting for the number of samples to process before a best frame is selected.
  • a determination may be based on achieving a score for a frame or object image in a frame that is above a predetermined threshold value.
  • the frame or frame comprising the object image that is above the threshold score will be selected (at 545).
  • process 500 determines that there are more portions of the electrical signal to be sampled, the process 500 samples (at 550) the next portion of the electrical signal representing the next frame of the video image presented on the display. The process 500 then returns to detect (at 515) object image(s) within the next frame.
  • the process may receive user input to stop the video capture at any point while process 500 is running. Specifically, the process is not confined to receiving user input to halt video capture after the feedback is displayed (at 530); the user input may be received at anytime while the process 500 is running. In such aspects, if at least one object image has been scored, then the process 500 will still select (at 545) the highest scoring object image. However, if no object images were scored, then the process will simply end.
  • the process 500 may optionally use the object image(s) detected in the previous sample to estimate the locations of the object images in the sample. Using this approach optimizes processing time when the process can determine that the mobile apparatus is relatively stable. For instance, the mobile apparatus may concurrently store gyro accelerometer data. The process 500 may then use gyro accelerometer data retrieved from the mobile apparatus to determine whether the mobile apparatus has remained stable and there is a greater likelihood that the object image(s) will be in similar locations. Thus, when the process 500 can determine that the mobile apparatus is relatively stable, the processing time for license plate detection may be increased because less of the portion of the electrical signal that represents the video image would need to be searched for the license plate image.
  • the process 500 may not use information about object image(s) from the previous frame as a predictor. Instead, the process 500 may undergo the same detection and scoring process discussed above. Then, for each object image that overlaps an object image detected in a previous frame (e.g., the object images share similar pixels either by space and/or location in the frames), the previous frame receives a higher score. Information about the overlapping object image(s) may be maintained for optimized processing later on. Additionally, in some aspects of the apparatus, the license plate detection apparatus may maintain a table of matching object image(s) for the sampled portions of the electrical signal representing frames of video images over time.
  • some object image(s) may exist in one or a few of the frames or some may exist in many or all frames and accordingly with higher scores.
  • all of the overlapping object images may be processed as discussed in greater detail in the foregoing sections and provided to the server for OCR or identification. This would lead to greater accuracy in actual license plate detection and OCR results.
  • the process 500 processes (at 555) the electrical signal based on the information associated with the selected frame to recover license plate information.
  • the process 500 determines (at 560) whether license plate information was recovered from the electrical signal.
  • the process 500 transmits (at 570) the license plate information to a remote server.
  • the process 500 receives (at 575) vehicle configuration information corresponding to the vehicle license plate.
  • the process 500 receives (at 580) user input to post the vehicle configuration information to a website.
  • the process 500 ends.
  • the process 500 displays (at 565) an alert that license plate information cannot be recovered.
  • an alert may guide the user to acquiring better video that is more likely to produce a readable license plate image.
  • the alert may guide the user's mobile device position or angle.
  • the process 500 may then return to collect additional video.
  • FIG. 6 illustrates an exemplary embodiment of a system architecture of a license plate detection apparatus.
  • the plate detection apparatus may be a mobile apparatus such as the apparatus 130 described with respect to FIG. 1 or any other suitable mobile apparatus that has image capture and processing capabilities.
  • the license plate detection apparatus includes an image capture apparatus 605, an imager
  • the license plate detection apparatus may communicate with a server having OCR Module 660, and an OCR analytics storage 670. However, in some aspects of the apparatus, the OCR module and/or OCR analytics storage may be part of the mobile apparatus.
  • the license plate detection apparatus illustrated in FIG. 6 generates license plate information 675, which may be routed through the gateway 645 processed for delivering the processed information VIN decoder 695.
  • the vehicle posting engine 699 may use information from the VIN decoder 695.
  • the VIN decoder 695 may be used to obtain a vehicle configuration from a license plate image.
  • the vehicle posting engine 699 may be used to post the obtained vehicle configuration information.
  • the VIN decoder 695 may be tied to service that provides vehicle configuration information, which is accessible via an API.
  • the vehicle posting engine 699 may also communicate with external web hosts via an API.
  • the image capture apparatus 605 communicates an optical image to the imager
  • the image capture apparatus 605 may comprise a camera lens and/or a camera that is built into a mobile apparatus.
  • the imager 610 may comprise a CMOS array, NMOS, CCD, or any other suitable image sensor that converts an optical image into an electrical signal (e.g., raw image data).
  • the electrical signal comprises pixel data associated with the captured image. The amount of pixel data is dependent on the resolution of the captured image.
  • the pixel data is stored as numerical values associated with each pixel and the numerical values indicate characteristics of the pixel such as color and brightness.
  • the electrical signal comprises a stream of raw data describing the exact details of each pixel derived from the optical image.
  • the imager 610 may produce a digital view as seen through the image capture apparatus for rendering at the display 655.
  • the image capture apparatus 605 may be configured to capture video.
  • a timing circuit such as the strobe circuit 685, may communicate with the imager 610.
  • the strobe circuit 685 may sample (or clock) the imager 610 to produce a sampled electrical signal at some periodicity such as 24-30 fps.
  • the sampled electrical signal may be representative of a frame of video presented on the display 655.
  • the electrical signal may be provided to the frame buffer 690.
  • the imager 610 may communicate the electrical signal directly to the format converter 620 when a single optical image is captured.
  • the frame buffer may communicate the sample of the electrical signal representative of the frame of video from the frame buffer to the format converter 620.
  • the frame buffer 690 may be bypassed such that the sampled electrical signal is communicated directly to the format converter 620.
  • the format converter 620 generates or compresses the raw image pixel data provided in the electrical signal to a standard, space efficient image format.
  • the frame buffer 690 and format converter 620 may be reversed such that the sampled electrical signals are converted to a compressed standard video format before buffering.
  • the standard image and/or video format can be read by the following modules of the license plate detection apparatus. However, the following description will assume that the sampled electrical signals are buffered before any such format conversion.
  • the format converter 620 will be described in greater detail in FIG. 7.
  • the format converter 620 communicates the standard image file (or image) to the image filter 625.
  • the image filter 625 performs a variety of operations on the image to provide the optimal conditions to detect a license plate image within the image. Such operations will be described in greater detail in FIG. 8. However, if the image filter 625 determines that the image is too distorted, noisy, or otherwise in a condition that is unreadable to the point that any filtering of the image will not result in a viable image for plate detection, the image filter 625 will signal to the rendering module 650 to display an alert on the display 655 that the image is not readable. Alternatively, once the image is filtered, ideally the image should be in a state that is optimal for accurate license plate detection. Therefore, the image filter 625 will then communicate the filtered image to the license plate detector 630.
  • the license plate detector 630 is an integral module of license plate detection apparatus.
  • the plate detector 630 will process the image to detect the presence of a license plate image by implementing several processes which will be described in greater detail in FIG. 9.
  • the license plate detector 630 provides for optical character recognition of the license plate image.
  • the license plate detector may generate overlays such as rectangular boxes around object images it detects as potential or candidate license plate images.
  • the overlays may be transmitted as signals from the license plate detector 630 to the rendering module 650.
  • the rendering module may instruct the display 655 to display the overlays over the image received from the imager 610 so that the user of the mobile apparatus can receive visual guidance relating to what object images the license plate detection apparatus detects as candidate license plate images. Such information is useful in guiding the user to capture optical images that include the license plate image and provide a higher likelihood of accurate license plate information recovery.
  • the license plate detector 630 will determine which portion of the image (or electrical signal) is most likely a license plate image. The license plate detector 630 will then transmit only the license plate image portion of the image to the network 635 by way of the network interface 697. Alternatively, a user may skip the entire image conversion process and using the keypad 615, key in the license plate information, which is then transmitted over the network 635 by way of the network interface 697. The network 635 then transmits the license plate image information (or image file) or keyed information to the network interface 640, which transmits signals to the gateway 645.
  • the gateway 645 may transmit the license plate image data to the OCR module 660.
  • OCR module 660 derives the license plate information such as state and number information from the license plate image.
  • the OCR module 660 may use several different third party and/or proprietary OCR applications to derive the license plate information.
  • the OCR module 660 may use information retrieved from the OCR analytics storage 670 to determine which OCR application has the greatest likelihood of accuracy in the event that different OCR applications detected different characters. For instance, the OCR analytics storage 670 may maintain statistics collected from the user input received at the apparatus 300 described with respect to FIG. 3A. The OCR module 660 may then select the license plate information that is statistically most likely to be accurate using information retrieved from the analytics storage 670.
  • the OCR module 660 may then transmit the license plate information 675 as a text string or strings to the gateway 645, which provides the license plate information 675 to the rendering module 650 through the network 635.
  • the rendering module 650 may then instruct the display 655 to display the license plate information 675.
  • the display 655 may then display a message similar to the one described with respect to FIG. 3A.
  • the license plate information 675 may be transmitted through the gateway
  • the gateway 645 may then transmit the processed information to the VIN Decoder 695.
  • the VIN decoder 695 may communicate with at one least third party service by way of an API to receive vehicle configuration information and to post the configuration information.
  • the configuration information may be transmitted back to the gateway 645 for further processing, which may include transmitting the vehicle configuration to the vehicle posting engine 699 to post the vehicle configuration information to a website.
  • the gateway 645 may transmit the vehicle configuration information to the rendering module 650 through the network 635.
  • the rendering module 650 may then instruct the display 655 to display the vehicle configuration information along with any other information to assist the user of the mobile apparatus.
  • the vehicle posting engine 699 may communicate with various internet services that specialize in used car sales.
  • the OCR module 660 or the license plate detector 630 will signal an alert to the rendering module 650, which will be rendered on the display 655.
  • the OCR module 660 may be located on an apparatus separate from an external server.
  • the OCR module 660 may be located on the mobile apparatus 130 similar to the license plate detection apparatus.
  • the format converter 620, image filter 625, and license plate detector 630 may be located on an external server and the electrical signal recovered from the optical image may be transmitted directly to the network 635 for processing by the modules on the external server.
  • the license plate detection apparatus provide several advantages in that it is not confined to still images. As discussed above, buffered or unbuffered video may be used by the license plate detection apparatus to determine the frame with the highest likelihood of having a license plate image. It also enables optical images to be taken while a mobile apparatus is moving and accounts for object images recovered from any angle and/or distance. Additionally, the license plate detection apparatus also provides the added benefit of alerting the user when a license plate image cannot be accurately detected in addition to guidance relating to how to get a better image that is more likely to produce license plate information. Such guidance may include directional guidance such as adjusting the viewing angle or distance as well as guidance to adjust lighting conditions, if possible. Thus, the license plate detection apparatus provides a solution to the complicated problem of how to derive license plate information captured from moving object images and from virtually any viewing angle. The license plate information may then be used to derive different information associated with the license plate information such an estimated value for a vehicle.
  • FIG. 7 illustrates an exemplary embodiment of a diagram of the format converter 620.
  • the format converter 620 receives the input of an electrical signal that defines an image 720 or an electrical signal that defines a sequence of sampled images such as video frames 725.
  • the format converter 620 outputs an image file 730 in a standard format such as the formats discussed above with respect to FIG. 1.
  • the format converter 620 includes a frame analyzer 715 and a conversion engine 710. When an electrical signal defining an image 720 is received at the format converter 620, the electrical signal will be read by the conversion engine 710.
  • the conversion engine 710 translates the pixel data from the electrical signal into a standard, compressed image format file 730.
  • Such standard formats may include jpeg, .png, .gif, .tiff or any other suitable image format similar to those discussed with respect to FIG. 1.
  • the standard format may include .mpeg, .mp4, .avi, or any other suitable standard video format. Since the electrical signal received at the format converter 620 is raw data which can make for a very large file, the conversion engine may compress the raw data into a format that requires less space and is more efficient for information recovery.
  • the format converter 620 may also receive a several sampled electrical signals, each representing frames of video images, such as frame data 725.
  • the video data frames may be received at the frame analyzer 715 in the format converter 620.
  • the frame analyzer 715 may perform a number of different functions. For instance, the frame analyzer 715 may perform a function of analyzing each frame and discarding any frames that are blurry, noisy, or generally bad candidates for license plate detection based on some detection process such as the process 500 described in FIG. 5. Those frames that are suitable candidates for license plate detection may be transmitted to the conversion engine 710 and converted to the standard format image 730 similar to how the image 720 was converted.
  • FIG. 8 illustrates an exemplary embodiment of a diagram of the image filter 625.
  • the image filter 625 receives a formatted image file that the image filter 625 is configured to read.
  • the image filter 625 outputs a filtered image 840 which may be optimized for more reliable license plate recognition.
  • the image filter 625 may signal an alert 845, indicating to the user that the image is unreadable and/or guide the user to capture a better image.
  • the image filter 625 includes a filter processor 805, a grayscale filter 810, and a parameters storage 835.
  • the filter processor 805 will retrieve parameters from the parameters storage 835, which will assist the filter processor 805 in how to optimally filter the image. For instance, if the received image was taken in cloudy conditions, the filter processor 805 may adjust the white balance of the image based on the parameters retrieved from the parameters storage 835. If the image was taken in the dark, the filter processor 805 may use a de-noise function based on the parameters retrieved from the parameters storage 835 to remove excess noise from the image.
  • the filter processor 805 also has the ability to learn based on the success of previously derived license plate images what parameters work best or better in different conditions such as those conditions described above. In such aspects, the filter processor 805 may take the learned data and update the parameters in the parameters storage 835 for future use.
  • the filter processor 805 also has logic to determine if an image will be readable by the license plate detector 630. When the filter processor 805 determines that the image will not be readable by the license plate detector 630, the filter processor 805 may signal an alert 845 to the rendering module 650. However, when the filter processor 805 determines that sufficient filtering will generate a readable image for reliable license plate detection, the filter processor 805 communicates the image, post filtering, to the grayscale filter 810.
  • the image filter 625 may receive several images in rapid succession. Such instances may be frames of a video that may be captured while a mobile apparatus is moving. In such instances, the filter processor 805 may continuously adjust the filter parameters to account for each video frame, it receives. The same alerts may be signaled in real-time in the event that a video frame is deemed unreadable by the filter processor 805.
  • the grayscale filter 810 will convert the received image file to grayscale. More specifically, the grayscale filter will convert the pixel values for each pixel in the received image file 830 to new values that correspond to appropriate grayscale levels. In some aspects of the filter, the pixel values may be between 0 and 255 (e.g., 256 values or 2 8 values). In other aspects of the filter, the pixel values may be between 0 and any other value that is a power of 2 minus 1, such as 1023, etc.
  • the image is converted to grayscale, to simplify and/or speed up the license plate detection process. For instance, by reducing the number of colors in the image, which could be in the millions, to shades of gray, the license plate image search time may be reduced.
  • the grayscale filter 810 ultimately produces the filtered image 840.
  • the image filter may first convert the image to grayscale using the grayscale filter 810 and then filter the grayscale image at the filter processor 805.
  • the filter processor 805 then outputs the filtered image 840.
  • the image filter 625 and the format converter 620 may be interchangeable. Specifically, the order in which this image is processed by these two modules may be swapped in some aspects of the apparatus.
  • FIG. 9 illustrates an exemplary embodiment of a diagram of the license plate detector
  • the license plate detector 630 receives a filtered image 930 and processes the image to determine license plate information 935, which is may be a cropped image of at least one license plate image.
  • the license plate detector 630 comprises an object detector 905, a quad processor 910, a quad filter 915, a region(s) of interest detector 920, and a patch processor 925.
  • the license plate detector 630 provides the integral function of detecting a license plate image from an image at virtually any viewing angle and under a multitude of conditions, and converting it to an image that can be accurately read by at least one OCR application.
  • the license plate detector 630 receives the filtered image 930 at the object detector 905.
  • the object detector 905 may use a mathematical method, such as a Maximal Stable Extremal Regions (MSER) method, for detecting regions in a digital image that differ in properties, such as brightness or color, compared to areas surrounding those regions.
  • MSER Maximal Stable Extremal Regions
  • the detected regions of the digital image have some properties that are constant or vary within a pre-described range of values; all the points (or pixels) in the region can be considered in some sense to be similar to each other.
  • This method of object detection may provide greater accuracy in the license plate detection process than other processes such as edge and/or corner detection.
  • the object detector 905 may use edge and/or corner detection methods to detect object images in an image that could be candidate license plate images.
  • the object images detected by the object detector 905 will have a uniform intensity throughout each adjacent pixel. Those adjacent pixels with a different intensity would be considered background rather than part of the object image.
  • the object detector 905 will construct a process of applying several thresholds to the image. Grayscale images may have intensity values between 0 and 255, 0 being black and 255 being white. However, in some aspects of the apparatus, these values may be reversed with 0 being white and 255 being black.
  • An initial threshold is set to be somewhere between 0 and 255. Variations in the object images are measured over a pre-determined range of threshold values.
  • a delta parameter indicates through how many different gray levels a region needs to be stable to be considered a potential detected object image.
  • the object images within the image that remain unchanged, or have little variation, over the applied delta thresholds are selected as likely candidate license plate images.
  • small variations in the object image may be acceptable.
  • the acceptable level of variations in an object image may be programmatically set for successful object image detection.
  • the number of pixels (or area of the image) that must be stable for object image detection may also be defined. For instance, a stable region that has less than a threshold number of pixels would not be selected as an object image, while a stable region with at least the threshold number of pixels would be selected as an object image.
  • the number of pixels may be determined based on known values relating to the expected pixel size of a license plate image or any other suitable calculation such as a height to width ratio.
  • the object detector 905 may recognize certain pre-determined textures in an image as well as the presence of informative features that provide a greater likelihood that the detected object image may be a license plate image.
  • textures may be recognized by using local binary patterns (LBP) cascade classifiers.
  • LBP is especially useful in real-time image processing settings such as when images are being captured as a mobile apparatus moves around an area.
  • LBP cascade classifiers may be modified such that the method is optimized for the detection of candidate license plate images.
  • an LBP cascade classification positive samples of an object image are created and stored on the license plate detection apparatus. For instance, a sample of a license plate image may be used. In some instances multiple samples may be needed for more accurate object image detection considering that license plates may vary from state to state or country to country.
  • the apparatus will then use the sample object images to train the object detector 905 to recognize license plate images based on the features and textures found in the sample object images.
  • LBP cascade classifiers may be used in addition to the operations discussed above to provide improved detection of candidate license plate images.
  • the object detector 905 will pass information relating to the detected object images to the quad processor 910 and/or the quad filter 915.
  • the object images may not be of a uniform shape such as a rectangle or oval.
  • the quad processor 910 will then fit a rectangle around each detected object image based on the object image information provided by the object detector 905. Rectangles are ideal due to the rectangular nature of license plates. As will be described in the foregoing, information about the rectangles may be used to overlay rectangles on object images that are displayed for the user's view on a mobile apparatus.
  • the rectangle will be sized such that it fits minimally around each object image and all areas of the object image are within the rectangle without more additional background space than is necessary to fit the object image.
  • the license plate image may not be perfectly rectangular. Therefore, the quad processor 910 will perform a process on each object image using the rectangle to form a quadrilateral from a convex hull formed around each object image.
  • the quad processor 910 will use an process that fits a quadrilateral as closely as possible to the detected object images in the image. For instance, the quad processor 910 will form a convex hull around the object image.
  • a convex hull is a polygon that fits around the detected object image as closely as possible.
  • the convex hull comprises edges and vertices.
  • the convex hull may have several vertices.
  • the quad processor 910 will take the convex hull and break it down to exactly four vertices (or points) that fit closely to the object image.
  • FIGS. 10-12 illustrate the functionality of the quad processor 910.
  • FIG. 10 illustrates an exemplary embodiment of an object image 1005 with a convex hull 1010 fit around the object image 1005, and a blown up region 1015.
  • the convex hull 1010 comprises several edges and vertices including vertices A-D.
  • the convex hull 1010 may be modified such that only 4 vertices are used. For instance, as illustrated in FIG. 11, for each adjacent pair of points A-D in the convex hull 1010, the quad processor 910 will find a new point Z that maintains convexity and enclosure of the object image when B and C are removed.
  • FIG. 11 illustrates an exemplary embodiment of a method for forming a quadrilateral (shown in FIG. 12) from the convex hull 1010. The process repeats for each set of 4 points until the convex hull 1010 is compressed to only four vertices as illustrated in FIG. 12.
  • FIG. 12 illustrates an exemplary embodiment of the object image 1005 enclosed in a quadrilateral 1210.
  • Quadrilateral 1210 fits as closely to the object image 1005 as possible without any edge overlapping the object image 1005. Fitting a quadrilateral closely to an object image as illustrated by the FIGS. 10-12 provides the benefit of greater efficiency in the license plate detection process. As will be described below, the license plate detection apparatus will only search the portions of the image within the quadrilaterals for the presence of a license plate image.
  • the quad processor 910 has drawn efficient quadrilaterals around each of the detected object images, the coordinates of the quadrilaterals are passed to the quad filter 915 and/or the region(s) of interest detector 920.
  • the license plate detection apparatus first overlays rectangles around each detected object image.
  • the quad filter 915 may use the rectangle information (rather than the quadrilateral information) received from the object detector 905, such as the pixel coordinates of the rectangles in the image, and look for rectangles similar in size and that overlap. The quad filter 915 will then discard the smaller rectangle(s), while maintaining the biggest.
  • the quad filter 915 will use a mechanism to determine which rectangle is more likely to be a full license plate image and discard the less likely image within the other rectangle(s). Such mechanisms may involve textures and intensity values as determined by the object detector 905.
  • the quad filter 915 may alternatively or additionally search the quadrilateral generated by the quad processor 910 for duplicates and perform a similar discarding process. By filtering out the duplicates, only unique object images within the rectangles will remain, with the likelihood that at least one of those object images is a license plate image. Thus, at this point, the license plate detection apparatus will only need to search the areas within the rectangles or quadrilaterals for the license plate image.
  • the region(s) of interest detector 920 will then determine which of the object images are actually object images that that have similar proportions (e.g., height and width) to the proportions that would be expected for a license plate image.
  • a license plate typically is rectangular in shape.
  • the object image may appear more like a parallelogram or trapezoid.
  • skew or keystone trapezoidal shape
  • object images that have a skew factor and/or keystone below and/or above a threshold value are likely object images that do not have the proportions expected for a license plate image or would likely be unreadable. Since a license plate has an expected proportion a threshold skew factor and/or keystone may be set and any detected object image that has a skew factor and/or keystone indicating that the object image is not a readable license plate image will be discarded. For instance, license plate images with a high skew and/or high keystone may be discarded.
  • the skew and keystone thresholds may be determined by digitally distorting known license plate images with varying amounts of pitch and yaw to see where the identification process and/or OCR fails.
  • the threshold may also be dependent on the size of the object image or quadrilateral/trapezoid.
  • quadrilaterals or trapezoids must cover enough pixel space to be identified and read by the OCR software. Those that do not have a large enough pixel space, skew factors that are too high, and/or keystones that are too high would then be discarded as either being unlikely candidates for license plate images or unreadable license plate images.
  • the skew factor is computed by finding the distance between opposing vertices of the quadrilateral and taking the ratio of the shorter distance to the longer distance so that the skew factor is less than or equal to 1. Rectangles and certain parallelograms that are likely candidate license plate images will have a skew factor that is close to 0, while skewed parallelograms will have a high skew factor. Additionally, trapezoids that are likely candidate license plate images will have a keystone that is close to 0, while trapezoids that are unlikely candidate license plate images will have a high keystone. Therefore, object images with a high skew factor are discarded, while the parallelograms with a lower skew factor and trapezoids with a lower keystone are maintained.
  • a threshold skew and a threshold keystone may be defined.
  • parallelograms having a skew factor below the threshold are maintained while those above the threshold are discarded.
  • trapezoids having a keystone below the threshold are maintained while those above the threshold are discarded.
  • the parallelogram or trapezoid may be maintained or discarded depending on the design of the apparatus.
  • the remaining parallelograms and trapezoids are then dewarped.
  • the dewarping process is particularly important for the trapezoids because it is used to convert the trapezoid into a rectangular image.
  • the dewarping process uses the four vertices of the quadrilateral and the 4 vertices of an un-rotated rectangle with an aspect ratio of 2: 1 (width: height), or any other suitable license plate aspect ratio, to computer a perspective transform.
  • the aspect ratio may be pixel width: pixel height of the image.
  • the perspective transform is applied on the region around the quadrilateral and the 2: 1 aspect ratio object image is cropped out.
  • the cropped object image, or patch is an object image comprising a candidate license plate image.
  • the patch is then provided to the patch processor 925, which will search for alpha numeric characters in the patch, find new object images within the patch, fit rectangles around those object images, and compute a score from the fit rectangles.
  • the score may be based on a virtual line that is drawn across the detected object images. If a line exists that has a minimal slope, the object images on that line may receive a score that indicates the object image is highly likely to be a license plate image. If no line with a minimal slope is detected, then an alert may be returned to the rendering module that a license plate image was not detected in the image. Scores may be calculated for several different patches from the same image and it follows that more than one license plate image may be detected in the same image.
  • the license plate information 935 may be transmitted to a server for OCR and further processing.
  • the license plate information is an image file comprising the license plate image. Additionally, the process for scoring the patch will be described in more detail with respect to FIG. 21.
  • FIG. 13 illustrates an exemplary embodiment of a diagram of the rendering module 650.
  • the rendering module 650 may receive as input alert information from the image filter 1335, or information about detected object images from the license plate detector 1330. The rendering module will then communicate rendering instructions 1340 to the display 655.
  • the rendering module 650 includes an overlay processor 1305, a detection failure engine 1310, and an image renderer 1315.
  • the overlay processor 1305 receives information about the detected object images 1330 from the license plate detector 630. As discussed above, such information may include coordinates of detected object images and rectangles determined to fit around those object images. The rectangle information is then provided to the detection failure engine 1310, which will determine that object images have been detected by the license plate detector 630. The detection failure engine 1310 may then forward the information about the rectangles to the image renderer 1315, which will provide rendering instructions 1340 to the display for how and where to display the rectangles around the image received from the imager 610. Such information my include pixel coordinates associated with the size and location of the rectangle and color information.
  • the rendering module 650 may instruct the display 655 to display the rectangle around the more likely object image in a way that is visually distinct from other rectangles. For instance, the rectangle around the object image more likely to be a license plate image may be displayed in a different color than the other rectangles in the display.
  • the license plate detector 630 may not detect any object images.
  • the overlay processor will not forward any rectangle information to the detection failure engine 1310.
  • the detection failure engine 1310 will then determine there has been an object image detection failure and signal an alert to the image renderer 1315.
  • the image renderer 1315 will then communicate the display rendering instructions 1340 for the alert to the display 655.
  • the license plate detection alerts have been described in greater detail above.
  • the image filter 625 may provide information to the image renderer 1315 indicating an alert that the captured image cannot be processed for some reason such as darkness, noise, blur, or any other reason that may cause the image to be otherwise unreadable.
  • the alert information from the image filter 625 is provided to the image renderer 1315, which then provides the rendering display instructions 1340 to the display 655 indicating how the alert will be displayed.
  • the image filter alerts have been discussed in detail above.
  • FIGS. 14-22 provide exemplary illustrations and processes detailing the functionality of the license plate detection module 630.
  • FIGS. 14-22 are devised to illustrate how the license plate detection apparatus goes from an optical image comprising many object images to detecting a license plate image among the object images.
  • FIG. 14 illustrates an exemplary embodiment of a scene 1400 that may be captured by a mobile apparatus 1410.
  • the mobile apparatus 1410 may be similar to the mobile apparatus 130 described with respect to FIG. 1.
  • the scene 1400 includes a structure 1425, a road 1430, a vehicle 1420, and a license plate 1405.
  • the mobile apparatus 1410 includes a display area 1415.
  • the mobile apparatus 1410 has activated the image capture functionality of the mobile apparatus 1410.
  • the image capture functionality may be an application that controls a camera lens and imager built into the apparatus 1410 that is capable of taking digital images.
  • the image capture functionality may be activated by enabling an application which activates the license plate detection apparatus capabilities described in FIG. 6.
  • the mobile apparatus 1410 may be capturing a still image, several images in burst mode, or video, in real-time for processing by the license plate detection apparatus.
  • the vehicle 1420 may be moving while the image capture process occurs, and/or the mobile apparatus may not be in a stationary position. In such instances, the license plate detection apparatus may determine the best video frame taken from the video.
  • FIG. 15 provides a high level illustration of an exemplary embodiment of how an image may be rendered on an apparatus 1500 by the license plate detection apparatus and transmission of a detected license plate image to a server 1550.
  • the apparatus 1500 includes a display area 1515 and an exploded view 1555 of the image that is rendered in display area 1515.
  • the exploded view 1555 includes object images 1525, rectangles 1520 that surround the object images, overlapping rectangles 1530, candidate license plate image 1505, and a rectangle 1510 that surrounds the candidate license plate image 1505.
  • the apparatus 1500 may wirelessly transmit license plate image data 1535 over the Internet 1540 to a server 1550 for further processing.
  • the license plate image data may be an image file comprising a license plate image.
  • the object detector 905 of the license plate detection apparatus has detected several object images 1525, as well as a candidate license plate image 1505.
  • the rendering module 650 has used information communicated from the license plate detector 630 to overlay rectangles around detected object images 1525 including the candidate license plate image 1505.
  • the rendering module 650 has also overlaid rectangles that differ in appearance around object images that are less likely to be license plate images. For instance, rectangles 1520 appear as dashed lines, while rectangle 1510 appears as a solid line.
  • the visual appearance of the rectangles is not limited to only those illustrated in exploded view 1555.
  • the visual appearance of the rectangles may differ by color, texture, thickness, or any other suitable way of indicating to a user that at least one rectangle is overlaid around an object image that is more likely to be a license plate image than the other object images in which rectangles are overlaid.
  • Exploded view 1555 also illustrates overlapping rectangles 1530.
  • the quad filter 915 of the license plate detector 630 may recognize the overlapping rectangles 1530 and discard some of the rectangles, and detected object images within those discarded rectangles, as appropriate.
  • the license plate detection apparatus has detected the presence of a candidate license plate image 1505 in the image.
  • the mobile apparatus 1500 will transmit the license plate image data 1535 associated with the license plate image over the internet 1540 to the server 1550 for further processing.
  • Such further processing may include OCR and using the license plate information derived from the OCR process to perform a number of different tasks that may be transmitted back to the mobile apparatus 1500 for rendering on the display area 1515.
  • the OCR capability may be located on the mobile apparatus 1500 itself.
  • the mobile apparatus may wirelessly transmit the license plate information such as state and number data rather than information about the license plate image itself.
  • FIG. 16 conceptually illustrates an exemplary embodiment of a more detailed process
  • the process 1600 for processing an electrical signal to recover license plate information as discussed at a high level in process 400.
  • the process may also be applied to detecting license plate information in a sample of an electrical signal representing a frame of a video image presented on a display as described in process 500 of FIG. 5.
  • the process 1600 may be performed by the license plate detection apparatus.
  • the process 1600 may begin after the mobile apparatus has activated an application, which enables the image capture feature of a mobile apparatus.
  • the process 1600 converts (at 1610) the captured image to grayscale. As discussed above, converting the image to grayscale makes for greater efficiency in distinguishing object images from background according to the level of contrast between adjacent pixels. Several filtering processes may also be performed on the image during the grayscale conversion process.
  • the process 1600 detects (at 1615) object image(s) from the grayscale image. Such object images may be the object images 1505 and 1525 as illustrated in FIG. 15.
  • the process 1600 processes (at 1620) a first object image. The processing of object images will be described in greater detail in the foregoing description.
  • the process 1600 determines whether an object image fits the criteria for a license plate image.
  • the process 1600 transmits (at 1630) the license plate image (or image data) to a server such as the server 1550.
  • a server such as the server 1550.
  • an object image fits the criteria for a license plate when a score of the object image is above a threshold value. Such a score may be determined by a process which will be discussed in the foregoing description.
  • the process 1600 determines (at 1635) whether there are more object images detected in the image and/or whether the object image being processed does not exceed a threshold score.
  • the process 1600 determines (at 1625) that an object image does not fit the criteria for a license plate image, the process 1600 does not transmit any data and determines (at 1635) whether more object images were detected in the image and/or whether the object image being processed did not exceed a threshold score.
  • the process 1600 determines that more object images were detected in the image and/or the object image being processed did not exceed a threshold score, the process 1600 processes (at 1640) the next object image. The process then returns to 1625 to determine if the object image fits the criteria of a license plate image.
  • the process 1600 determines (at 1645) whether at least one license plate image was detected in the process 1600.
  • the process ends.
  • an alert is generated (at 1650) and the rendering module 650 sends instructions to display a detection failure message at the display 655.
  • the detection failure alert may provide guidance to the user for capturing a better image. For instance, the alert may guide the user to move the mobile apparatus in a particular direction such as up or down and/or adjust the tilt of the mobile apparatus. Other alerts may guide the user to find a location with better lighting or any other suitable message that may assist the user such that the license plate detection apparatus has a greater likelihood of detecting at least one license plate image in an image.
  • the process 1600 may be performed in real-time. For instance, the process 1600 may be performed successively as more images are captured either by capturing several frames of video as the mobile apparatus or object images in the scene move and/or are tracked or by using an image capture device's burst mode.
  • the process 1600 provides the advantage of being able to detect and read a license plate image in an image at virtually any viewing angle and under a variety of ambient conditions. Additionally, the criteria for determining a license plate image is determined based on the operations performed by the license plate detector. These operations will be further illustrated in the following figures as well.
  • FIGS. 17-19 illustrate the operations performed by the quad processor 910. For instance,
  • FIG. 17 illustrates an exemplary embodiment of an image 1700 comprising a license plate image 1710.
  • the license plate may be affixed to a vehicle which would also be part of the image.
  • the image 1700 includes the license plate image 1710 and a rectangle 1705.
  • an object image has been detected by the object detector 905 of the license plate detector 630.
  • the object image in this example is the license plate image 1710.
  • the quad processor 910 fit a rectangle 1705 around the license plate image 1710.
  • Information associated with the rectangle may be provided to the rendering module 650 for overlaying a rectangle around the detected license plate image in the image displayed on the display 655.
  • FIG. 18 illustrates an exemplary embodiment of a portion of an image 1800 comprising the same license plate image 1710 illustrated in FIG. 17.
  • Image portion 1800 includes the license plate image 1810 and a quadrilateral 1805.
  • the quad processor 910 of the license plate detector 630 performs several functions to derive a quadrilateral that closely fits the detected object image. Once the quadrilateral has been derived, the quad processor 910 then computes the skew factor and/or keystone discussed above.
  • the region(s) of interest detector 920 can dewarp the image to move one step closer to confirming the presence of a license plate image in the image and to also generate patch that is easily read by OCR software.
  • the patch is the license plate image that has been cropped out of the image.
  • FIG. 19 is an exemplary embodiment of the dewarping process being performed on a license plate image 1905 to arrive at license plate image 1910.
  • FIG. 19 illustrates two stages
  • the first stage 1901 illustrates the license plate image 1905 in a trapezoidal shape similar to the shape of the quadrilateral 1805 illustrated in FIG. 18.
  • the second stage illustrates the license plate image 1905 in a trapezoidal shape similar to the shape of the quadrilateral 1805 illustrated in FIG. 18.
  • license plate image 1910 illustrates the license plate image 1910 after the dewarping process has been performed.
  • license plate image 1910 has undergone a perspective transform and rotation.
  • the license plate image 1910 as shown in the second stage 1902 is now in a readable rectangular shape.
  • the license plate image may also undergo corrections if the license plate image is skewed or may scale the license plate image to a suitable size.
  • the ability to accurately dewarp quadrilaterals and especially the quadrilaterals that are license plate images taken at any angle is an integral piece of the license plate detection apparatus.
  • the dewarping capability enables a user to capture an image of a license plate at a variety of different angles and distances. For instance, the image may be taken with any mobile apparatus at virtually any height, direction, and/or distance. Additionally, it provides the added benefit of being able to capture a moving image from any position.
  • the region(s) of interest detector 920 will crop the rectangular license plate image to generate a patch. The patch will be used for further confirmation that the license plate image 1910 is, in fact, an image of a license plate.
  • FIG. 20 conceptually illustrates an exemplary embodiment of a process 2000 for processing an image comprising a license plate image.
  • the process 2000 may be performed by a license plate detection apparatus.
  • the process 2000 may start after the license plate detection apparatus has instantiated an application that enables the image capture feature of a mobile apparatus.
  • the process 2000 detects (at 2010) at least one object image, similar to the object image detection performed by process 1600.
  • the following describes in greater detail the process of processing (at 1620) the image.
  • the process 2000 then fits (at 2015) a rectangle to each detected object image in order to reduce the search space to the detected object images.
  • the information associated with the rectangle may also be used as an overlay to indicate to users of the license plate detection apparatus the location(s) of the detected object image(s).
  • the process then uses the rectangles to form (at 2020) a convex hull around each object image.
  • the convex hull as discussed above, is a polygon of several vertices and edges that fits closely around an object image without having any edges that overlap the object image.
  • the process 2000 compresses the convex hull to a quadrilateral that closely fits around the detected object image.
  • the process of compressing the convex hull into a quadrilateral was discussed in detail with respect to FIGS. 9-12.
  • the process 2000 filters (at 2030) duplicate rectangles and/or quadrilaterals.
  • rectangles or quadrilaterals that are similar in size and overlap may be discarded based on some set criteria. For example, the smaller rectangle and/or quadrilateral may be discarded.
  • the process 2000 calculates (at 2035) a skew factor. The process 2000 then dewarps (at 2035).
  • the process crops (at 2045) the object image within the quadrilateral, which becomes the patch.
  • the patch will be used for further processing as discussed below.
  • the object image is cropped at a particular ratio that is common for license plates of a particular region or type. For instance, the process may crop out a 2: 1 aspect ratio patch, of the image, which is likely to contain the license plate image.
  • FIG. 21 illustrates an exemplary embodiment of a diagram that determines whether a patch 2100 is an actual license plate image.
  • the patch 2100 includes a candidate license plate image 2105, alpha-numeric characters 2120 and 2140, rectangles 2115, sloped lines 2130, zero- slope line 2110, and graphic 2125.
  • object images may be detected using the MSER object detection method. Conjunctively or conversely, some aspects of the apparatus, may use edge and or corner detection methods to detect the object images.
  • the detected object images are alpha-numeric characters 2120 and 2140 as well as graphic 2125.
  • a stroke width transform SWT may be performed to partition the detected object images into those that are likely from an alphanumeric character and those that are not.
  • the SWT may try to capture the only alpha-numeric effective features and use certain geometric signatures of alpha-numeric characters to filter out non-alpha-numeric areas, resulting in more reliable text regions.
  • the SWT transform may partition the alphanumeric characters 2120 and 2140 from the graphic 2125.
  • some object images other than alphanumeric characters may pass through the SWT partitioning. Thus, further processing may be necessary to filter out the object images that are not alpha-numeric characters and also to determine whether the alpha-numeric characters in the license plate image fit the characteristics common for a license plate images.
  • a line is fit to the center of the rectangle pair for each pair of rectangles. For instance, a sloped line is shown for the D and object image 2125 pair. The distance of all other rectangles to the lines 2130 and 2110 are accumulated and the pair with the smallest summed distance is used as a text baseline. For instance, the zero-slope line 2110 has the smallest summed distance of the rectangles to the line 2110.
  • Some aspects of the apparatus may implement a scoring process to determine the presence of a license plate image. For instance, some aspects of the scoring process may determine a score for the determined alpha-numeric characters on the zero- slope line 2110.
  • the score may increase when the rectangle around the alpha-numeric character is not rotated beyond a threshold amount.
  • the score may decrease if the detected alpha-numeric character is too solid.
  • solidity may be defined as the character area/rectangle area.
  • the patch score increases by some scoring value if the center of the rectangle is within a particular distance of the baseline line where X is the shorter of the rectangle height and width. For instance, if the particular distance were to be defined as the shorter of the rectangle height and width and if the scoring value is set at 1, the patch score value of the patch 2100 would be 7 because the rectangles around the characters "1DDQ976" are within a shorter distance than the width of the rectangle. Furthermore, the zero-slope of the line 2110 between the alpha-numeric characters 2120 further confirm that this patch is likely a license plate image since typically license plates have a string of characters along a same line.
  • Sloped lines 2130 are, therefore, unlikely to provide any indication that the patch is a license plate image because the distance between characters is too great and the slope is indicative of a low likelihood of a license plate image. Accordingly, in some aspects of the process, sloped lines 2130 are discarded in the process.
  • the patch when the patch has a score above a threshold value, the patch is determined to be a license plate image, and the license plate detection is complete.
  • the license plate image data is then transmitted to a server for further processing and for use in other functions computed by the server, the results of which are provided to the license plate detection apparatus.
  • FIG. 22 conceptually illustrates an exemplary embodiment of a process 2200 for processing a patch comprising a candidate license plate image such as patch 2100.
  • the process may be performed by the license plate detection apparatus.
  • the process may begin after a patch has been cropped from an image file.
  • the process 2200 processes (at 2205) only substantially rectangular portion(s) of the patch to locate alpha-numeric characters.
  • the process 2200 fits (at 2210) rectangles around the located alpha-numeric characters and computes scores based on the distances between rectangle pairs as discussed above with respect to FIG. 21.
  • the process 2200 selects (at 2215) the patch with the best score to recognize as a license plate image.
  • the process 2200 may select all patches that have a score above a threshold level to be deemed as license plate images. In such instances, multiple patches, or instances of license plate information, would be transmitted to the server for further processing.
  • FIG. 23 illustrates an exemplary embodiment of an operating environment 2300 for communication between a gateway 2395 and client apparatuses 2310, 2330, and 2370.
  • client apparatuses 2310, 2330, and 2370 communicate over one or more wired or wireless networks 2340 and 2360.
  • wireless network 2340 such as a cellular network
  • WAN wide area network
  • a mobile network gateway in some aspects of the service provides a packet oriented mobile data service or other mobile data service allowing wireless networks to transmit data to other networks, such as the WAN 2380.
  • access device 2360 (e.g., IEEE 802.1 1b/g/n wireless access device) provides communication access to the WAN 2380.
  • the apparatuses 2310, 2330, and 2370 can be any portable electronic computing apparatus capable of communicating with the gateway 2395.
  • the apparatuses 2310 and 2370 may have an installed application that is configured to communicate with the gateway 2395.
  • the apparatus 2330 may communicate with the gateway 2395 through a website having a particular URL.
  • the apparatus 2330 may be a non-portable apparatus capable of accessing the internet through a web browser.
  • the gateway 2395 may also communicate with third party services that provide vehicle configuration information from license plate information and/or additional vehicle related information. Such additional information may be a VIN number or vehicle configuration information. As shown, the gateway 2395 may communicate directly with at least one third party processing service 2390 if the service is located on the same network as the gateway 2395. Alternatively, the gateway 2395 may communicate with at least one of the third party processing services 2390 over the WAN 2380 (e.g., the internet). Additionally, the vehicle configuration information may be posted to a website by communicating with the WAN 2380.
  • third party processing service 2390 e.g., the internet
  • the process of posting vehicle configuration information to a website may incorporate the location with the vehicle.
  • the vehicle configuration information may be posted to a website that is used for listing a car for sale.
  • having the location information may be used by the listing website in searches performed by users of the website.
  • the apparatus may use location information acquired through a global positioning service (GPS) satellite 2320.
  • GPS global positioning service
  • the apparatuses 2310 and 2370 may be configured to use a GPS service and provide location information to the gateway 2395 using the connections discussed above.
  • the provided location information may be used by the gateway 2395 and provided to additional modules, discussed in the following figure, as necessary.
  • the service described in FIG. 23 provides greater accuracy when posting a vehicle configuration to a website because it also incorporates location information.
  • Such location information may also be used to determine an average local value of similar vehicles listed for sale or sold in the area. Additionally, the location information is transmitted without any interaction with the user, which provides greater ease in obtaining a more accurate value.
  • FIG. 24 illustrates an exemplary flow of data between a gateway 2400 and various other modules.
  • the gateway 2400 and modules 2410-2460 may be located on a server such as the server 230.
  • the gateway 2400 may be a request router in that it receives requests from the various modules 2410-2460 and routes the requests to at least one of the appropriate module 2410-2460.
  • the gateway 2400 communicates with various modules 2410-2460, which may communicate with various third party services to retrieve data that enables the gateway 2400 to provide an estimated value for a vehicle to a client apparatus 2470 from an image of a license plate.
  • the client apparatus 2470 may use a network interface 2460 to transmit at least one license plate image recovered from an optical image taken by the client apparatus 2470.
  • the client apparatus 2470 may include an installed application providing instructions for how to communicate with the gateway 2400 through the network interface 2460.
  • the network interface 2460 provides license plate image information or text input of a license plate to the gateway 2400.
  • the network interface 2460 may transmit text strings received as user input at the client apparatus 2470 or a license plate image processed by the client apparatus 2470 to the gateway 2400.
  • the gateway 2400 may route the license plate image data to the OCR module 2410 to perform the OCR text extraction of the license plate information.
  • the OCR module 2410 may have specialized or a commercial OCR software application installed that enables accurate extraction of the license plate number and state.
  • the OCR module may be similar to the OCR module discussed in FIG. 6.
  • the OCR module 2410 may also have the capability of determining if a license plate image contains a clear enough image that will provide for accurate text extraction. In this example, if the license plate image does not contain a clear image or the image quality is too low, the OCR module may alert the gateway 2400 to transmit a warning to the client apparatus 2470. In an alternative example, a license plate image may be recovered and transmitted to the gateway 2400 for further processing.
  • the gateway 2400 will provide the extracted text to a translator 2420, which is capable of determining a VIN from the license plate information.
  • the translator 2420 may communicate with third party services using functionality provided in an application programming interface (API) associated with the third party services. Such services may retrieve VINs from license plate information.
  • API application programming interface
  • the various modules 2410-2450 may also be configured to communicate with the third party services or apparatuses (not shown) using APIs associated with the third party services. In such aspects of the service, each module 2410-2450 may route a request through the gateway 2400 to the network interface 2460, which will communicate with the appropriate third party service (not shown).
  • the gateway 2400 then routes the retrieved VIN to the VIN decoder 2430 along with a request to generate a VIN explosion.
  • the VIN decoder 2430 is capable of using the VIN to generate a VIN explosion by requesting the VIN explosion from a third party service.
  • the VIN explosion includes all of the features, attributes, options, and configurations of the vehicle associated with the VIN (and the license plate image).
  • the VIN explosion may be provided as an array of data, which the gateway 2400 or VIN decoder 2430 is capable of understanding, processing, and/or routing accurately.
  • the VIN decoder 2430 may communicate with a third party service by using an API associated with the service.
  • the VIN and/or vehicle data derived from the VIN explosion may be routed back through the gateway 2400 and through the network interface 2460 to the client apparatus 2470.
  • the client apparatus may display the vehicle configuration data derived from the VIN explosion.
  • the vehicle configuration information from the VIN explosion may also be routed from the gateway to the vehicle posting engine 2450 with a request to post the vehicle configuration information to a website.
  • the vehicle posting engine 2450 may then communicate with at least one web host via an API to post the vehicle configuration to the website.
  • Such websites may include services that list vehicles that are for sale.
  • the apparatus may provide an estimated value of the vehicle acquired from a value estimation service. The estimated value may be adjusted as additional information about the vehicle is received at the apparatus 2470 via user input. Such information may include vehicle mileage, condition, features and/or any other pertinent vehicle information.
  • the vehicle posting engine 2450 may also use geographic location information provided by the apparatus 2470 in posting to the website.
  • FIG. 25 conceptually illustrates an exemplary process 2500 for receiving vehicle configuration information and posting a vehicle for sale from a license plate image.
  • the process 2500 may be performed by a server such as the server 230.
  • the seller's mobile apparatus e.g., mobile apparatus 2310
  • gateway 2395 which includes a number of modules, located on a server, such as the server 230.
  • the customized software application downloaded to the user's mobile device 2310 can receive user inputs (e.g., vehicle license plate image data) and transmit this information to gateway 2395, which is located on server 230 that includes the requisite hardware and software configured to perform the process illustrated in FIG. 25.
  • the server 230 can include one or more of the hardware components shown in FIG. 30 and described below.
  • the process may begin after the mobile apparatus 2310 has recovered a suitable license plate image for transmission to the server 230.
  • the process 2500 receives (at 2510) license plate image information or text input from a mobile apparatus.
  • the text input may be information associated with a vehicle license plate such as a state and alphanumeric characters.
  • the process 2500 automatically requests (at 2530) a VIN associated with the license plate information.
  • server 230 is configured to automatically transmit a request for a VIN corresponding to the license plate information by sending the request to a third party processing service.
  • the process communicates with the third party service (e.g., service 2390) by using an API.
  • the third party service can identify the vehicle and VIN using the license plate image information and confirm whether the vehicle is in fact recognized by the service.
  • the process 2500 receives the VIN associated with the license plate information.
  • the process 2500 requests (at 2550) a vehicle configuration using the received VIN.
  • the vehicle configuration may include different features and options that are equipped on the vehicle. For instance, such features and options may include different wheel sizes, interior trim, vehicle type (e.g., coupe, sedan, sport utility vehicle, convertible, truck, etc.), sound system, suspension, and any other type of vehicle configuration or feature.
  • the vehicle configuration can also include estimated pricing information from a third-party website, for example.
  • the process 2500 receives (at 2570) the vehicle configuration data. If the service is unable to identify the vehicle, the server may transmit an error message back to the mobile device request another image of the license plate, as described above.
  • the process 2500 transmits the vehicle configuration data to the mobile apparatus 2310.
  • the mobile apparatus 2310 may display the configuration information for the user of the apparatus to view.
  • the vehicle configuration information can include estimated value information from a third party service that provides an estimated price of the vehicle using the VIN number and any identified vehicle history, including accidents, repairs, and the like.
  • the price information can include a variation of prices including trade-in value, estimated sales prices (i.e., obtained from third party pricing services based on VIN number, for example), and a dealership price.
  • the server 230 is configured to automatically transmit the year, make, model and other information (e.g., mileage) to a third-party valuation service that automatically returns an estimated value of the vehicle.
  • the server 230 is configured to maintain its own database of vehicle says based on year, make, model, geographical region, time of year, and the like, all of which can be provided with actual historical sales prices.
  • Server 230 is configured to automatically adjust the recommended price received from the third-party valuation service based on estimated odometer, estimated condition, estimated vehicle configuration package, and the like. Then, upon receiving the prospective seller's vehicle information, the server 230 is configured to reference this database and identify one or more closest matches to generate an estimated sales price. In either case, this information is transmitted to the seller at step 2580.
  • the user may then wish to post the configuration information to a website such as a sales listing website.
  • the process 2500 receives (at 2585) a selection to post the vehicle information to the website.
  • the vehicle information posted may include vehicle information that was included as part of the received vehicle configuration information as well as additional configuration details which may have been added or adjusted at the mobile apparatus by user input.
  • the process 2500 then communicates (at 2590) the vehicle information and instructions to post the information to an external website. As discussed above, this may be handled by communicating using a website's API.
  • the customized software application downloaded on the mobile apparatus facilitates the posting and sale of the user's vehicle through the Internet.
  • the software application can include a listing of vehicles that the user is attempting to sell (i.e., a "garage" of vehicles).
  • the user of the mobile device is required to create and/or access a user account associated with a software application running on the mobile device. To do so, the user can create a user account by verifying and accepting conventional legal terms and conditions as well as privacy policies.
  • the user can select to post the vehicle at step 2585 and related information, such as vehicle configuration information (make, model, mileage, etc.) and requested price on the website and/or via the software application.
  • the software application can provide a website and/or marketplace that lists all vehicles that have been posted by users of the software application for sale.
  • gateway 2395 communicates with a plurality of third party retail sites (e.g., Craigslist®) to post the vehicle for sale.
  • the initial listing of the vehicle includes a total price and suggested monthly payments for a prospective buyer.
  • the retail website further provides user modifiable filters that enables prospective buyers to filter by price, year, mileage, make, style, color, physical location of the vehicle, and the like. It should be appreciated that other social networks can also facilitate the posting and sale of the user' s vehicles (e.g., step 2590).
  • FIG. 26 conceptually illustrates a flowchart for a method 2600 for transmitting confirmation of vehicle ownership from a license plate image.
  • the process 2600 may be performed by a server such as the server 230.
  • the described process is a refinement to the method described above.
  • the server 230 i.e., gateway 2395
  • the text input may be information associated with a vehicle license plate such as a state and alpha-numeric characters.
  • the process 2600 then requests (at 2630) a VIN associated with the license plate information.
  • the process 2600 may request the VIN by sending the request to a third party server.
  • the process 2600 communicates with the third party server by using an API.
  • the process 2600 receives the VIN associated with the license plate information.
  • the process 2600 then requests (at 2650) vehicle ownership information using the received VIN. As described above, this request may be prompted by the software application on the mobile device in advance of the user's request to post the vehicle configuration information to a website and/or the software application for sale.
  • the requested vehicle ownership information can include one or more of the user' s personal information, such as at least one of a first and last name, full name, address, and driver's license number. Additional information can include the vehicle's title (e.g., a PDF or JPEG image of the title), loan payment information, registration information, and the like.
  • the process 2600 receives this vehicle ownership information at step
  • the process 2600 receives information from a driver' s license image from the mobile apparatus and requests (at 2685) validation that the driver's license information matches the vehicle owner' s information.
  • the process determines if the information matches. When the information does not match, the process 2600 transmits (at 2687) an alert to the mobile apparatus indicating that the registration information does not match the driver's license information. In this case, the process ends and the user can repeat the vehicle ownership process. Alternatively, when the vehicle ownership information does match, the process 2600 transmits (at 2695) confirmation of vehicle ownership to the apparatus.
  • the software application enables the user to post the vehicle for sale as described above according to the exemplary aspect.
  • FIG. 27A conceptually illustrates a flowchart for a method 2700 for a prospective buyer to view the vehicle information and electronically transmit a purchase offer according to an exemplary aspect.
  • the prospective buyer can execute the transaction entirely using his or her mobile apparatus, which can be mobile apparatus 2370, for example.
  • the seller via mobile apparatus 2310
  • buyer via mobile apparatus 2370
  • gateway 2395 can be located on server 230, as described above, and configured to execute the process shown in FIG. 27A.
  • the seller can post the vehicle and related information to a website or the like using the software application on the seller's mobile device, for example, to formally present the vehicle for sale.
  • This process is described above with respect to steps 2580-2590 of FIG. 25.
  • this posting includes a number of photographs captured by the seller of the vehicle at different angles.
  • the display on the screen of the seller's mobile device may present a number of exemplary photograph angles for the seller to mimic when capture images of the vehicle. These multiple vehicle images can then be uploaded to server 230 for presentation when the vehicle and related information are posted to the website.
  • the prospective buyer can visit the website and/or a "marketplace" provided on the downloadable software application to view from a number of available vehicles for sale. For example, if the prospective buyer is browsing vehicles on a third party service, such as Craigslist, and identifies the seller's vehicle, the listing my include a prompt for the buyer to download the customized software application on his or her mobile device 2370.
  • the additional vehicle information will also be available to the buyer, including car history, location, etc., as described above.
  • a user interface of the presentation is discussed below with respect to FIG. 27B.
  • each presented vehicle includes a button, link or the like (e.g., "Message Seller") that enables the prospective buyer to initiate a confidential and private communication with the seller.
  • a button, link or the like e.g., "Message Seller”
  • the buyer can select the user input entry at step 2715 to initiate the private communication.
  • a private messaging platform is created by the software application installed on both the seller's mobile device and the buyer's mobile device to facilitate a sales negotiation between the two parties. It should be appreciated that this message center enables the buyer to ask any questions relating to the vehicle, the vehicle's condition and location, and the like, as well as an ability to negotiate the purchase price.
  • each mobile apparatus of the seller and buyer can transmit GPS or other device location information to the server 230.
  • the server 230 can access a map database to identify one or a few meeting locations (e.g., local coffee shop, school parking lot, etc.) that are mutually convenient for each of the buyer and seller.
  • the identified location can be at a distance approximately equal length of travel from each of the buyer and seller.
  • the test drive location can predefined. In either instant, the server 230 can then transmit the suggested test drive location as well as more or more times to the mobile apparatuses of each of the buyer and seller to facilitate the scheduling of a test.
  • step 2730 if the buyer and seller agree to the vehicle's sale, using the software application, either or both parties can transmit a signal to the gateway 2395 and/or server 230 that indicates that the parties have agreed on a sale and that the purchasing steps should now be initiated on the buyer's side.
  • this communication can further prompt the seller to indicate whether the vehicle should be removed from the website and/or marketplace of the software application.
  • the step of removing the vehicle from these one or more platforms performed automatically.
  • FIG. 27B illustrates a user interface of a buyer's mobile device for viewing the vehicle information and electronically transmitting a purchase offer according to an exemplary aspect.
  • the user interface may be provided by the customized software application that can be downloaded from server 230, as described herein.
  • the user interface 2750 can include one or a plurality of digital images 2751 of the vehicle uploaded by the seller's mobile apparatus to server 230.
  • the user interface 2750 includes general vehicle information 2752, including make, model and total mileage of the vehicle.
  • the buyer is presented with both a total asking prices 2753 and suggested monthly payment prices 2754 if the buyer were to finance the purchase of the vehicle. For more detailed purchase options as discussed below, the buyer can select a "purchase options" input.
  • the buyer can initiate a private communication with the seller (step 2720) using the "message seller" button 2755.
  • the buyer can also request the presentation of additional vehicle information 2757, including "Details", “Pricing” and the like, for example.
  • FIGS. 28A and 28B conceptually illustrate a flowchart for a method 2800 for finalizing and executing a vehicle sale according to an exemplary aspect.
  • the seller and buyer have previously agreed upon a sales price and have communicated this agreement to the server 230, for example, by the software application downloaded on one of the buyer's and/or seller's respective mobile apparatuses (or alternatively, by a website, for example).
  • purchasing options can be presented to the buyer of the vehicle at step 2805. More particularly, the buyer is presented with an option to selected a financing plan and, therefore, undergo an underwriting at step 2810.
  • the software application determines that the agreement is based on a cash offer and requests the buyer to enter a value of the cash offer at step 2815. Otherwise, if the buyer selects financing, the buyer is prompted with a request for minimal information to facilitate the underwriting.
  • the prospective buyer is prompted to scan an image of his or her license (or other user identification), including the barcode of the identification and to also indicate a current annual income.
  • the buyer can take a picture (using the mobile apparatus, for example) of the driver' s license at step 2820 and send this scan to a server (e.g., server 230) via gateway 2395 for processing.
  • a server e.g., server 230
  • the server 230 can then access one or more third-party credit evaluation services to execute a credit report on the buyer. Based on the credit report, the server 230 can automatically define financing terms for the buyer, which may include, for example, monthly payment, interest rates of the loan, down payment (if any) and the like. These payment terms are defined by server 230 at step 2830 and then presented to the user for review at step 2835. For example, the defined financing terms can be displayed to the user at step 2835 using the software application on the buyer' s mobile device.
  • the buyer is presented with a number of customizable loan options. For example, if the user elects to pay a larger down payment, the interest rate of the load may decrease. Moreover, the buyer can easily define the term of the loan, and the like. It should be appreciated that the variation of these options will be dependent on the user' s credit, annual income, and the like, and preferably set by the server 230, automatically.
  • the dynamic rate pricing can be adjusted by the buyer using one or more slides presented on the user interface of the mobile apparatus. Thus, in one example, as the buyer increase the down payment using an interface slide, the interest rate may decrease proportionately.
  • server 230 automatically, sends a formal offer to the seller, such that the seller is presented with this offer on the seller's mobile apparatus by the software application.
  • the software application on the seller' s mobile apparatus 2310 requests acceptance of the buyer' s offer at step 2840. If the seller rejects the buyer' s offer, the process will either end or return to step 2815. In one aspect, the seller can then be provided with an option to define a new cash offer, defining a new/increased sales price, for example. This process can be continually repeated where the buyer can then accept and submit a new offer (again at step 2835) for the seller's acceptance. Once the seller accepts the buyer's offer at step 2840 or vice versa, the server 230 creates, approves, and executes a loan agreement (if required) for the buyer.
  • FIG. 28B further conceptually illustrates a flowchart for a method
  • the software application on the buyer's mobile apparatus e.g., mobile device 2370
  • This information can include one or more of proof of car insurance, bank account information, proof of employment and/or pay stub, power of attorney (if required), and other personal information. This information can then be transmitted using the buyer' s mobile apparatus to server 230.
  • the server 230 uses credit analysis methods (which can be flexible based on the amount of risk the financing company is willing to accept) to determine whether the buyer is approved at step 2855.
  • this analysis is performed automatically by server 230, for example, but can also be performed by a system administrator, loan underwriter, or the like.
  • step 2860 the transaction is canceled and both the buyer and the seller are notified of the canceled transaction by an alert sent to each user respective mobile apparatus, where the software application displays the cancellation notifications.
  • the software application includes an indication explaining the reasoning for the transaction cancellation (e.g., buyer loan failed). If the buyer is approved, the method proceeds to step 2865 where the loan is presented to the buyer. In one optional aspect, the method can also proceed to step 2870, where the server 230 can again access third party services, such as credit services to confirm the buyer's credit.
  • step 2875 the system (i.e., at server 230) is presented with an option of a "hard pull" meaning the system can automatically, or at the instruction of a system administrator, pull the financing offer. If this option is executed, the method again proceeds to step 2860 where each party is notified of the canceled transaction as described above.
  • step 2880 the system confirms to both the seller and buyer on the respective mobile software applications that the loan has been approved and proceeds with the steps need to execute the loan agreement at step 2880.
  • steps mainly include presenting a formal loan offer to the buyer that includes the financial terms of the load, all relevant conditions, and request the user to confirm acceptance of the loan agreement. If the user accepts the loan, of which acceptance can be electronic signature, the method proceeds to finalize the transaction and transfer title, as will be discussed as follows.
  • FIG. 29A conceptually illustrates a flowchart for a method 2900 for finalizing a vehicle transaction according to an exemplary aspect.
  • the server 230 receives notification from the buyer that the loan agreement has been executed/accepted by the buyer.
  • the buyer using the software application on his or her mobile device 2370, has agreed to all terms and conditions offered by the server 230.
  • gateway 2395 for example, server 230 creates a bill of sale to be signed by each of the buyer and seller and sends confirmation, including the bill of sale, to the seller at step 2915 and buyer at step 2910. It should be understood that these steps can be performed in series or parallel.
  • the seller is prompted to transfer keys to the buyer at step 2920.
  • the server 230 transfers title and executes the loan at step 2925.
  • the server can perform a transfer of the total sales amount to the seller's requested bank account.
  • the server can withdraw the agreed down payment from the buyer's bank account.
  • the title transfer can be performed by a system administrator, for example.
  • the system administrator can schedule payment with the current lien holder and accept transfer of title. The title can then be held be the service until the buyer fully satisfies loan obligations.
  • the buyer can be prompted with the option to purchase a warranty for the vehicle at step 2930. If the buyer declines the warranty, the process ends. Otherwise, the server 230 receives the request a purchases the warranty from the vehicle manufacturer, for example, on behalf of the buyer. Conventional systems require the purchaser to contact the vehicle manufacturer or dealer directly to purchase the warranty. The exemplary method performs this task on behalf of the buyer at step 2935. It should be appreciated that according to an alternative aspect, the option to purchase a warranty can be presented during the loan application process, the prices of which will be included in the proposed loan plan.
  • FIG. 29B illustrates user interfaces for each of the buyer and seller for finalizing a vehicle transaction according to an exemplary aspect.
  • the seller's mobile apparatus 2310 is presented with an interface 2950A that presents a "Final Checklist" of action items to complete including, for example, "Sign Title", “Exchange Title”, “Emissions Certificate”, “Remove Plates", and "Bill of Sale”.
  • the buyer's user interface 2950B is presented with the same actions items in this example.
  • each software application will send a confirmation signal back to server 230 as discussed above.
  • the ACH transfer payment of the sales price will be initiated into the seller's account and, if applicable, the down payment will be withdrawn from the buyer's bank account.
  • FIG. 29C illustrates a screen shot 2950C of an administrator for finalizing a vehicle transaction according to an exemplary aspect.
  • administrators of the service perform certain actions in order to facilitate the vehicle sale and loan agreement.
  • the administrator is continuously provided with a dynamic snapshot of the developing relationship between the buyer and seller.
  • the administrator is presented with both the seller's name 2951 and information (e.g., address, email, and the like) as well as the buyer's name 2952 and information (e.g., address, email, and the like).
  • the total loan price 2953 is presented, which can be a derivation of the offer price, warranty and fees minus any down payment.
  • the vehicle information 2954 including images, and car details is also presented on user interface 2950C. Furthermore, the status of all required seller documents 2955 and buyer documents 2956 is presented. Although not shown, certain action items can be presented in one aspect enabling the administrator to prompt the buyer and/or seller to submit required documentation. Furthermore, warranty information 2957 can be presented on the user interface. According to the exemplary aspect, the administrator is enabled to continuously determine the current state of the transaction, and, if necessary, perform any required actions to prompt either the buyer and/or seller to move the transaction along in the process.
  • FIG. 30 illustrates an exemplary embodiment of a system 3000 that may implement the license plate detection apparatus and/or vehicle transaction process according to an exemplary aspect.
  • the electronic system 3000 of some embodiments may be a mobile apparatus of the seller and/or buyer as described above.
  • the system 3000 (or certain components shown therein) can be used to implement the remote server 230 (and gateway 2395) configured to perform the processes described herein to electronically facilitate the vehicle transaction.
  • the electronic system includes various types of machine readable media and interfaces.
  • the electronic system includes a bus 3005, processor(s) 3010, read only memory (ROM) 3015, input device(s) 3020, random access memory (RAM) 3025, output device(s) 3030, a network component 3035, and a permanent storage device 3040.
  • the bus 3005 communicatively connects the internal devices and/or components of the electronic system. For instance, the bus 3005 communicatively connects the processor(s) 3010 with the ROM 3015, the RAM 3025, and the permanent storage 3040. The processor(s) 3010 retrieve instructions from the memory units to execute processes of the invention.
  • the processor(s) 3010 may be implemented with one or more general-purpose and/or special-purpose processors. Examples include microprocessors, microcontrollers, DSP processors, and other circuitry that can execute software. Alternatively, or in addition to the one or more general-purpose and/or special-purpose processors, the processor may be implemented with dedicated hardware such as, by way of example, one or more FPGAs (Field Programmable Gate Array), PLDs (Programmable Logic Device), controllers, state machines, gated logic, discrete hardware components, or any other suitable circuitry, or any combination of circuits.
  • FPGAs Field Programmable Gate Array
  • PLDs Programmable Logic Device
  • a storage medium may be any available medium that can be accessed by the processor(s) 3010.
  • machine-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a processor.
  • any connection is properly termed a machine-readable medium.
  • machine-readable media may comprise non-transitory machine-readable media (e.g., tangible media).
  • machine-readable media may comprise transitory machine-readable media (e.g., a signal). Combinations of the above should also be included within the scope of machine-readable media.
  • multiple software inventions can be implemented as subparts of a larger program while remaining distinct software inventions.
  • multiple software inventions can also be implemented as separate programs. Any combination of separate programs that together implement a software invention described here is within the scope of the invention.
  • the software programs when installed to operate on one or more electronic systems 3000, define one or more specific machine implementations that execute and perform the operations of the software programs.
  • the ROM 3015 stores static instructions needed by the processor(s) 3010 and other components of the electronic system.
  • the ROM may store the instructions necessary for the processor(s) 3010 to execute the processes provided by the license plate detection apparatus.
  • the permanent storage 3040 is a non-volatile memory that stores instructions and data when the electronic system 3000 is on or off.
  • the permanent storage 3040 is a read/write memory device, such as a hard disk or a flash drive. Storage media may be any available media that can be accessed by a computer.
  • the ROM could also be EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
  • the RAM 3025 is a volatile read/write memory.
  • the RAM 3025 stores instructions needed by the processor(s) 3010 at runtime, the RAM 3025 may also store the real-time video images acquired during the license plate detection process.
  • the bus 3005 also connects input and output devices 3020 and 3030.
  • the input devices enable the user to communicate information and select commands to the electronic system.
  • the input devices 3020 may be a keypad, image capture apparatus, or a touch screen display capable of receiving touch interactions.
  • the output device(s) 3030 display images generated by the electronic system.
  • the output devices may include printers or display devices such as monitors.
  • the bus 3005 also couples the electronic system to a network 3035.
  • the electronic system may be part of a local area network (LAN), a wide area network (WAN), the Internet, or an Intranet by using a network interface.
  • the electronic system may also be a mobile apparatus that is connected to a mobile data network supplied by a wireless carrier.
  • Such networks may include 3G, HSPA, EVDO, and/or LTE.
  • FIGS. 31 A and 31B conceptually illustrate a detailed flowchart for a method 3100A for processing a prospective buyer for facilitating a vehicle sale according to an exemplary aspect.
  • the method shown in these figures corresponds to one or more steps of FIGS. 28A and 28B described above.
  • the prospective buyer is prompted to enter his or her identification information, which can include scan an image of his or her license (or other user identification), including the barcode of the identification captures by a camera of his or her electronic device, for example.
  • the buyer is prompted to indicate a current employment status, including annual income.
  • the buyer can take a picture (using the mobile apparatus, for example) of the driver's license at step 3102 and send this scanned data to a server (e.g., server 230) via gateway 2395 for processing.
  • a server e.g., server 230
  • the server 230 is configured to perform an automatic determination of whether the buyer is within a predetermined age range (i.e., 18 to 100). For example, based on an image capture of the buyer's driver's license (e.g., the barcode from the license as discussed above) or other identification results in the creation of data records corresponding to the buyer's identity, the server can subsequently perform automatic processing steps without manual intervention.
  • server 230 identifies the age and automatically compares it with an age range that is predetermined by an administrator, for example.
  • the server automatically can perform a determination as to whether the buyer is within the predetermined age range by processing of the buyer's identification results in determining the age range and then applying a numerical calculation to confirm the buyer is over age 18, but under 100, for example. If the buyer is not within this range, the buyer is automatically declined at step 3106.
  • the server 230 proceeds to step 3108 and automatically performs a soft credit pull of the buyer.
  • the server 230 can access one or more third-party credit evaluation services to obtain a credit report (e.g., a credit score) of the buyer.
  • a credit report e.g., a credit score
  • the server is configured to automatically assign the buyer with a "buying power" at step 3110, which can be communicated to the buyer in one exemplary aspect.
  • the details of the buyer power calculation will be discussed in more detail below.
  • server 230 is configured to automatically generates the buying power tier by numerically manipulating the buyer's credit score and placing it within one of a number of calculated tiers (e.g., five tiers).
  • any credit score over 750 can assigned to Tier 1, any score between 675 and 750, can be assigned to Tier 2, and so forth.
  • These numbers are simply provided as an example, but can be varied according to the system administrator.
  • one or more algorithms or equations may be applied to assign a unique buying power tier for the buyer, which may also take into consideration other data points which are retrieved for the buyer.
  • other factors may be applied within the algorithms/equations that may alter the exact tier in which the buyer is placed. These other factors could include a debt to income ratio, the geographical location where the buyer is found, the employment status of the buyer, annual income, among others. Therefore, it should be understood that the buying power tier that is assigned is not only an automatic aspect of the invention, but may take into account a number of different attributes of the buyer that go beyond the mere evaluation of the buyer's credit score.
  • the server 230 is able to use the determined buying power for the buyer to establish three limits for the buyer's financing plan: (1) maximum monthly payment; (2) maximum total loan amount; and (3) ratio of maximum loan to vehicle value.
  • This information is stored in a database at server 230 and is associated with the prospective buyer accordingly.
  • this step in the method comprises one or more algorithms which automatically determine the acceptable finance terms. Variables within the algorithms include ranges of values which enable finance terms to be determined without manual intervention or evaluation.
  • the buyer uses his application to select a vehicle for potential purchase. This process is described in detail above and will not be repeated herein.
  • the server 230 calculates finance plan limits for the selected vehicle. In particular, the vehicle is assigned with a selling price and/or an estimated price as described above. Based on the calculated buying power of the buyer, the server 230 is configured to automatically determine acceptable ranges of finance terms for the buyer that meet the value of the specific car with the maximum monthly payment, maximum total loan amount, and ratio of maximum loan to vehicle value identified above.
  • FIGS. 32A-32F illustrate screenshots of a user interface for buyer to select financing and payment terms according to an exemplary aspect.
  • the buyer is presented with estimated prices for "trade-in", "private party” and "retail”.
  • the buyer is also presented with a user controllable slider that enables the buyer to quickly and easily adjust the offered price to the seller.
  • the scale of the adjustable slider will be based around the seller's asking price (e.g., the seller's asking price could be the high price of the slider).
  • FIG. 32B the buyer is presented with a user controllable slider that enables the user to adjust the amount he or she would like to pay as a down payment.
  • the APR will adjust according to the amount of down payment.
  • the buyer can buy down the APR by increasing the down payment.
  • FIGS. 32D and 32E illustrate the buyer's ability to set the amount of monthly payment and define the total length of the term.
  • FIG. 32F presents an interface that is a snapshot of the buyer's financing plan.
  • the buyer's "buying power" defines the terms of the required down payment, monthly payment, and the like.
  • the server 230 is configured to access a predefined matrix in a database and compare the buying power with the vehicle information, including asking price, estimated price and/or the like, set forth in the matrix.
  • This predefined matrix further provides the maximum and minimum values for each slide shown in FIGS. 32A-32E.
  • the matrix effectively provides one or more algorithms or equations that enable the minimum and maximum values for each slide to be automatically generated on the buyer's mobile device.
  • a user in a Tier 1 buying power will be required to put less money down for a specific vehicle than buyer in Tier 5, for example. All of these terms are automatically defined by server 230 and presented to the user as adjustable financing and payment options.
  • server 230 automatically performs another check at step 3118 to confirm that the buyer's selected terms are acceptable for the limits of the selected vehicle. If not, the buyer is prompted to select new terms again at step 3116. Otherwise, at step 3120, the formal offer is automatically presented on the seller's software application. If the seller rejects this offer at step 3122, the method proceeds to step 3124 where the buyer is informed that his offer has been rejected (e.g., the offered prices is too low) and the buyer is presented with the option of submitting a new offer. For example, in one aspect, the method can return to step 3116.
  • step 3122 if the seller accepts the buyers offer at step 3122, the method proceeds to step
  • the server 230 again accesses one or more third-party credit evaluation services to obtain a full credit report including a credit score of the buyer. For example, referring to FIG. 31C, the server 230 can again determine the buying power of the buyer at step 3130. As shown, the method proceeds to step 3132 to determine if the credit report in the database of server 230 is current, i.e., less than a predetermined threshold (e.g., 30 days) old. If not, the server 230 accesses one or more third-party credit evaluation services at step 3136 to obtain a full credit report including a credit score of the buyer.
  • a predetermined threshold e.g. 30 days
  • step 3138 The buyer power of the buyer is then validated at step 3138 before the method resumes at step 3134. Finally, referring back to FIG. 31B, if the buyer's buying power and credit have been validated, the method automatically proceeds to step 3128 where the transaction between the buyer and seller is finalized. The execution of the transaction is described above with respect to FIG. 29A according to one exemplary aspect and will not be repeated herein.
  • FIG. 3 ID illustrates a detailed flowchart of a method for calculating and updating a buyer's "buying power" according to an exemplary aspect.
  • this method can be performed to calculate the buying power during either of step 3110 and/or step 3126 as described above.
  • the server 230 is prompted to automatically calculate or update the buyer's buying power.
  • the buyer may be automatically declined. For example, if based on the buyer's credit report the credit score is below a predetermined threshold, if the buyer's annual income is below a predetermined level, if the buyer is under 18 years old, or the like, the buyer can be automatically declined.
  • step 3144 server 230 can wait a predetermined time period before notifying the buyer and providing the buyer with the option to contact an administrator at step 3146 and seek override of this decision. If the buyer does not seek such override, the method proceeds to step 3148 where all further transactions with this buyer are canceled.
  • step 3150 the server 230 is configured cross reference a number of condition rules to determine whether the buyer has correctly been assigned the current tier (e.g., Tier 1). For example, one conditional rule may be whether the buyer has defaulted on any vehicle loan payment for more than 60 days within the past year. Based on the buyer's credit report previously obtained, the server 230 is configured to automatically determine this fact and, if so, the method will proceed to step 3156 where the buyer will be reduced to a lower tier of buying power. For example, if the above noted condition is satisfied, the server 230 will include a condition that the buyer is reduced by one tier. Step 3152 then determines whether the buying power has actually been reduced by one or more tiers.
  • condition rules e.g., Tier 1
  • one conditional rule may be whether the buyer has defaulted on any vehicle loan payment for more than 60 days within the past year.
  • the server 230 is configured to automatically determine this fact and, if so, the method will proceed to step 3156 where the buyer will be reduced to a lower
  • step 3154 the transactional process continues at its current step as shown in FIGS. 31A and 31B. Otherwise, if the buying power has been reduced by a tier, the method proceeds to step 3158, where the server 230 automatically holds the off and provides the user with the option to reset the financing terms based on the current tier (i.e., the updated calculated buying power). Finally, at step 3160, the user is either updates the financing and payment terms (e.g., step 3116 described above) and the process continues at step 3154, or the transaction is canceled at step 3148.
  • the server 230 automatically holds the off and provides the user with the option to reset the financing terms based on the current tier (i.e., the updated calculated buying power).
  • step 3160 the user is either updates the financing and payment terms (e.g., step 3116 described above) and the process continues at step 3154, or the transaction is canceled at step 3148.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Multimedia (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Software Systems (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un système et un procédé permettant d'identifier automatiquement un véhicule et de faciliter une transaction associée au véhicule. Le système comprend un premier appareil mobile avec un capteur d'image qui capture une image optique d'une plaque d'immatriculation de véhicule et convertit l'image optique en un signal électrique, un détecteur de plaque d'immatriculation qui identifie les informations de plaque d'immatriculation de véhicule, et une interface qui transmet les informations de plaque d'immatriculation de véhicule. Le système comprend en outre un serveur distant qui identifie automatiquement des informations de configuration de véhicule sur la base des informations de plaque d'immatriculation de véhicule, transmet automatiquement les informations de configuration de véhicule au premier dispositif mobile, et fournit automatiquement des informations de ventes de véhicule d'accès en réponse à une requête de publication provenant du premier appareil mobile. En outre, un second appareil mobile affiche les informations de ventes de véhicule et déclenche automatiquement la transaction associée au véhicule en réponse à une entrée d'utilisateur.
PCT/US2017/063755 2016-11-29 2017-11-29 Système et procédé de traitement électronique de transactions de véhicule sur la base d'une détection d'image d'une plaque d'immatriculation de véhicule Ceased WO2018102440A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CA3045147A CA3045147A1 (fr) 2016-11-29 2017-11-29 Systeme et procede de traitement electronique de transactions de vehicule sur la base d'une detection d'image d'une plaque d'immatriculation de vehicule

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US15/363,960 2016-11-29
US15/363,960 US9818154B1 (en) 2014-06-27 2016-11-29 System and method for electronic processing of vehicle transactions based on image detection of vehicle license plate

Publications (1)

Publication Number Publication Date
WO2018102440A1 true WO2018102440A1 (fr) 2018-06-07

Family

ID=62241866

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2017/063755 Ceased WO2018102440A1 (fr) 2016-11-29 2017-11-29 Système et procédé de traitement électronique de transactions de véhicule sur la base d'une détection d'image d'une plaque d'immatriculation de véhicule

Country Status (2)

Country Link
CA (1) CA3045147A1 (fr)
WO (1) WO2018102440A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220153281A1 (en) * 2019-08-14 2022-05-19 Honda Motor Co., Ltd. Information provision system, information terminal, and information provision method

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113706881B (zh) * 2021-07-30 2022-06-07 郑州信大捷安信息技术股份有限公司 一种基于可见光的车辆套牌检测系统及方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130085935A1 (en) * 2008-01-18 2013-04-04 Mitek Systems Systems and methods for mobile image capture and remittance processing
US20150125041A1 (en) * 2013-11-06 2015-05-07 Xerox Corporation Reinforcement learning approach to character level segmentation of license plate images
US9031948B1 (en) * 2011-07-06 2015-05-12 Shawn B. Smith Vehicle prediction and association tool based on license plate recognition
US20170193320A1 (en) * 2014-06-27 2017-07-06 Blinker, Inc. Method and apparatus for providing loan verification from an image
US9818154B1 (en) * 2014-06-27 2017-11-14 Blinker, Inc. System and method for electronic processing of vehicle transactions based on image detection of vehicle license plate

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130085935A1 (en) * 2008-01-18 2013-04-04 Mitek Systems Systems and methods for mobile image capture and remittance processing
US9031948B1 (en) * 2011-07-06 2015-05-12 Shawn B. Smith Vehicle prediction and association tool based on license plate recognition
US20150125041A1 (en) * 2013-11-06 2015-05-07 Xerox Corporation Reinforcement learning approach to character level segmentation of license plate images
US20170193320A1 (en) * 2014-06-27 2017-07-06 Blinker, Inc. Method and apparatus for providing loan verification from an image
US9818154B1 (en) * 2014-06-27 2017-11-14 Blinker, Inc. System and method for electronic processing of vehicle transactions based on image detection of vehicle license plate

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220153281A1 (en) * 2019-08-14 2022-05-19 Honda Motor Co., Ltd. Information provision system, information terminal, and information provision method
US12151686B2 (en) * 2019-08-14 2024-11-26 Honda Motor Co., Ltd. Information provision system, information terminal, and information provision method

Also Published As

Publication number Publication date
CA3045147A1 (fr) 2018-06-07

Similar Documents

Publication Publication Date Title
US11436652B1 (en) System and method for electronic processing of vehicle transactions based on image detection of vehicle license plate
US9818154B1 (en) System and method for electronic processing of vehicle transactions based on image detection of vehicle license plate
US10176531B2 (en) Method and apparatus for receiving an insurance quote from an image
US10803496B1 (en) Systems and methods for implementing machine vision and optical recognition
US10540564B2 (en) Method and apparatus for identifying vehicle information from an image
US11625791B1 (en) Feedback loop in mobile damage assessment and claims processing
US10885371B2 (en) Method and apparatus for verifying an object image in a captured optical image
US11361385B2 (en) Application facilitated claims damage estimation
US9607236B1 (en) Method and apparatus for providing loan verification from an image
US10169675B2 (en) Method and apparatus for receiving listings of similar vehicles from an image
US10204282B2 (en) Method and apparatus for verifying vehicle ownership from an image
US10163026B2 (en) Method and apparatus for recovering a vehicle identification number from an image
US9824453B1 (en) Three dimensional image scan for vehicle
US20180158039A1 (en) Mobile Vehicle Acquisition System and Method
US10515285B2 (en) Method and apparatus for blocking information from an image
US20170140238A1 (en) Method and apparatus for receiving a location of a vehicle service center from an image
US20170372141A1 (en) Method and apparatus for obtaining a vehicle history report from an image
AU2020313995A1 (en) Asset verification systems and/or methods
US10192130B2 (en) Method and apparatus for recovering a vehicle value from an image
WO2018102440A1 (fr) Système et procédé de traitement électronique de transactions de véhicule sur la base d'une détection d'image d'une plaque d'immatriculation de véhicule
US10210417B2 (en) Method and apparatus for receiving a refinancing offer from an image
US10210396B2 (en) Method and apparatus for receiving vehicle information from an image and posting the vehicle information to a website
KR102108508B1 (ko) 스마트 할부 금융 서비스 시스템

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17876211

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 3045147

Country of ref document: CA

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17876211

Country of ref document: EP

Kind code of ref document: A1