CN116805400A - Pedestrian crosswalk area identification method and device and electronic equipment - Google Patents
Pedestrian crosswalk area identification method and device and electronic equipment Download PDFInfo
- Publication number
- CN116805400A CN116805400A CN202210265954.XA CN202210265954A CN116805400A CN 116805400 A CN116805400 A CN 116805400A CN 202210265954 A CN202210265954 A CN 202210265954A CN 116805400 A CN116805400 A CN 116805400A
- Authority
- CN
- China
- Prior art keywords
- pedestrian crossing
- road surface
- area
- crossing area
- image
- 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.)
- Pending
Links
Landscapes
- Image Analysis (AREA)
Abstract
本申请实施例提供了一种人行横道区域的识别方法、装置及电子设备，包括：获取交通路路侧摄像头拍摄的路面的视频流；从所述视频流中提取出包含人行横道区域的路面图像，标记所述人行横道区域在所述路面图像中的坐标范围；基于傅里叶变换根据所述坐标范围从所述路面图像中识别所述人行横道区域。
Embodiments of the present application provide a method, device and electronic equipment for identifying a crosswalk area, including: acquiring a video stream of the road surface captured by a camera on the side of the traffic road; extracting a road surface image containing the crosswalk area from the video stream, and marking The coordinate range of the crosswalk area in the road surface image; the crosswalk area is identified from the road surface image according to the coordinate range based on Fourier transform.
Description
Technical Field
The application relates to the technical field of internet of vehicles, in particular to a pedestrian crossing area identification method and device and electronic equipment.
Background
With the development of the vehicle-road cooperative technology, the automatic driving and the intelligent network driving need to pay attention to the high-incidence areas of safety accidents such as lanes, crosswalks and waiting areas. Therefore, the accurate identification of the lane, the crosswalk and the region to be turned can effectively improve the calculation efficiency of the vehicle safety early warning.
In a scene of identifying a pedestrian crosswalk area, a pedestrian crosswalk area is often defined by using a manual drawing mode in a vehicle-road cooperation holographic intersection system, and then the pedestrian crosswalk area is assisted by using a scene algorithm to identify, remind and early warn, but the method needs manual early drawing, is time-consuming and labor-consuming, and cannot timely cope with a scene of redefining the pedestrian crosswalk, so that the detection accuracy of the pedestrian crosswalk area is poor.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for identifying a crosswalk region and electronic equipment, so as to solve the problem of poor accuracy of crosswalk region detection.
In order to solve the technical problems, the embodiment of the application is realized as follows:
in a first aspect, an embodiment of the present application provides a method for identifying a crosswalk area, including: acquiring a video stream of a road surface shot by a traffic road side camera; extracting a pavement image containing a crosswalk region from the video stream, and marking the coordinate range of the crosswalk region in the pavement image; the crosswalk region is identified from the road surface image based on a fourier transform from the coordinate range.
In a second aspect, an embodiment of the present application provides a device for identifying a crosswalk area, including: the acquisition module is used for acquiring the video stream of the road surface shot by the traffic road side camera; the extraction module is used for extracting a pavement image containing a crosswalk region from the video stream and marking the coordinate range of the crosswalk region in the pavement image; and the identification module is used for identifying the crosswalk area from the pavement image according to the coordinate range based on Fourier transformation.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus; the processor, the communication interface and the memory complete communication with each other through a communication bus; the memory is used for storing a computer program; the processor is configured to execute the program stored in the memory, to implement the step of the method for identifying a crosswalk area according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for identifying a crosswalk region according to the first aspect.
According to the technical scheme provided by the embodiment of the application, the pavement image containing the pedestrian crossing area is extracted from the video stream by acquiring the video stream of the pavement shot by the traffic road side camera, the coordinate range of the pedestrian crossing area in the pavement image is marked, the pedestrian crossing area is identified from the pavement image according to the coordinate range based on Fourier transformation, the pedestrian crossing area in the pavement can be automatically identified according to the acquired actual pavement video stream, the manual early-stage drawing of the pedestrian crossing area is not needed, time and labor are saved, the pedestrian crossing area can be identified again according to the acquired video stream when the pedestrian crossing is repainted, and the detection accuracy of the pedestrian crossing area is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a first method for identifying a crosswalk area according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a second method for identifying a crosswalk area according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a third method for identifying a crosswalk area according to an embodiment of the present application;
fig. 4 is a schematic block diagram of a device for identifying a crosswalk area according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The embodiment of the application provides a method and a device for identifying a crosswalk region and electronic equipment, and solves the problem of poor accuracy of crosswalk region detection.
In order to make the technical solution of the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, shall fall within the scope of the application.
As shown in fig. 1, an exemplary embodiment of the present application provides a method for identifying a crosswalk area, where an execution subject of the method may be a server, where the server may be an independent server or may be a server cluster formed by a plurality of servers, and the server may be a server capable of identifying a crosswalk area, and the method for identifying a crosswalk area may specifically include the following steps:
in step S101, a video stream of a road surface captured by a traffic road side camera is acquired.
Specifically, a camera on the traffic road side near the pavement area is used to collect a video stream of the pavement, and the video stream includes pavement information of the pavement, pedestrians and vehicles on the pavement, and the like. When road surface information of a road is acquired, a camera can be controlled to shoot the road surface through a traffic signal lamp trigger mechanism of a pedestrian road, namely, for a certain crosswalk, according to a signal control period of the traffic signal lamp, the camera is controlled to sample the lighting time of a red light and a green light of the traffic signal lamp as a time period, namely, when the traffic signal lamp is red light and green light, the camera is controlled to sample, so that the video stream of the most or the least of pedestrians and vehicles can be acquired.
In step S103, a road surface image including a crosswalk region is extracted from the video stream, and the coordinate range of the crosswalk region in the road surface image is marked.
Specifically, after a video stream of a road surface is collected, the video stream is input into a DarkNet deep learning model, a moving object in the video stream is subjected to contour recognition through the DarkNet deep learning model, a plurality of images output by the DarkNet deep learning model are obtained, and the images are subjected to image filling, wherein the image filling process specifically comprises the following steps: filtering the moving objects in the images, and color filling the filtered areas of the moving objects to obtain pavement images containing pedestrian crosswalk areas, wherein the moving objects include but are not limited to pedestrians, vehicles and the like. And (3) obtaining only a pavement and a pavement marking area in the pavement image containing the pedestrian crossing area, then carrying out binarization on the pavement image according to the length, the width, the color and other characteristics of the pedestrian crossing line, transversely traversing the whole pavement image, and marking the coordinate range of the binarized pedestrian crossing area in the pavement image according to the pedestrian crossing characteristics, wherein the coordinate range comprises but is not limited to the range from the upper left coordinate to the lower right coordinate of the pedestrian crossing area, or the range from the lower left coordinate to the upper right coordinate.
Further, strong chromatic aberration exists between the pavement and the pavement marking, so that the influence of illumination on the recognition accuracy of the pedestrian crossing area is avoided, and the pavement marking can be subjected to uniform gray scale processing on the whole image in the image filling process or after the image filling process, so that the interference of illumination is eliminated, and the recognition accuracy of the pedestrian crossing area is improved.
In step S105, a crosswalk region is identified from the road surface image based on the fourier transform from the coordinate range.
Specifically, after the coordinate range of the crosswalk region in the road surface image is determined, the approximate range of the crosswalk region is obtained, and the boundary image of the crosswalk region is extracted from the road surface image based on the approximate range of the crosswalk region based on fourier transform. Specifically, a target image in a coordinate range in a road surface image is subjected to low-pass filtering through Fourier transform, the target image after the low-pass filtering is subjected to inverse Fourier transform, and a crosswalk region is identified from the target image, namely, a crosswalk region in the road surface image is identified, so that the crosswalk region is identified from the road surface image.
The fourier transform is specifically as follows:
assuming that F (x, y) is an M by N two-dimensional discrete signal obtained by sampling the road surface image at equal intervals in the above embodiment, F (u, v) represents fourier transform of F (x, y), and the coordinate system where F (u, v) is located is referred to as a frequency domain. Wherein, the two-dimensional discrete signal is a gray level distribution value of the road surface image, M represents the column number of the road surface image, N represents the line number of the road surface image, x and y are discrete real variables, u and v are discrete frequency variables, and the two-dimensional discrete Fourier transform is defined as follows:
in the processing of the pavement image, taking M equal to N, the two-dimensional discrete Fourier transform is defined as:
wherein,,is a positive transformation kernel, < >>Is an inverse transform kernel, defining the square of the spectrum of the two-dimensional discrete fourier transform as the power spectrum P (u, v) of f (x, y), specifically as follows:
P(u,v)＝|F(u,v)| 2 ＝R 2 (u,v)+I 2 (u, v) wherein R 2 (u, v) is the square of the real part of F (u, v), I 2 (u, v) is the square of the imaginary part of F (u, v).
The power spectrum reflects the distribution condition of the energy of the two-dimensional discrete signal on the space frequency domain, the power spectrum P (u, v) is used as a threshold value, when the identified pavement area is unreasonable, the boundary of the pavement area can be re-extracted by adjusting the power spectrum, and the value range of the power spectrum P (u, v) in the embodiment of the application can be [127, 255]. And performing low-pass filtering on the pavement image through Fourier transformation, and performing inverse Fourier transformation to obtain a boundary feature picture of the pavement area in the pavement image, namely identifying the pavement area in the pavement image. The pavement image is processed in a Fourier transform mode, so that the computational complexity of the traditional identification of the pavement area is reduced, and the boundary detection of the pavement area is performed by adopting the whole pavement image, so that the identification accuracy of the pavement area is further improved.
According to the technical scheme provided by the embodiment of the application, the pavement image containing the pedestrian crossing area is extracted from the video stream by acquiring the video stream of the pavement shot by the traffic road side camera, the coordinate range of the pedestrian crossing area in the pavement image is marked, the pedestrian crossing area is identified from the pavement image according to the coordinate range based on Fourier transformation, the pedestrian crossing area in the pavement can be automatically identified according to the acquired actual pavement video stream, the manual early drawing of the pedestrian crossing area is not needed, time and labor are saved, the pedestrian crossing area can be identified again according to the acquired video stream when the pedestrian crossing is repainted, the detection accuracy of the pedestrian crossing area is improved, and the limitation of later maintenance is reduced.
As shown in fig. 2, an embodiment of the present application provides a method for identifying a crosswalk area, where an execution subject of the method may be a server, where the server may be an independent server or may be a server cluster formed by a plurality of servers, and the server may be a server capable of identifying a crosswalk area, and the method for identifying a crosswalk area specifically may include the following steps: :
in step S201, a video stream of a road surface captured by a traffic road side camera is acquired.
In step S203, a road surface image including a crosswalk region is extracted from the video stream, and the coordinate range of the crosswalk region in the road surface image is marked.
In step S205, a crosswalk region is identified from the road surface image based on the coordinate range based on the fourier transform.
In step S207, whether the identified crosswalk region is correctly delineated is verified by hough transform, and if the verification is not passed, the crosswalk region is re-identified, and if the verification is passed, the crosswalk region is correctly delineated.
Specifically, verifying whether the identified crosswalk region is properly delineated by hough transform includes: converting the image space pixel coordinates of the boundary line of the identified crosswalk region into polar coordinates, wherein the angle value of the polar coordinates is the inclination angle of the zebra stripes in the identified crosswalk region; calculating the marking boundary coordinates of the identified crosswalk region according to the image space pixel coordinates of the boundary line of the identified crosswalk region and the inclination angle range of the zebra stripes; calculating the zebra crossing boundary coordinates in the identified crosswalk region according to the image space pixel coordinates of the boundary line of the identified crosswalk region and the attribute characteristics of the zebra crossing; redefining a target crosswalk region based on the marking boundary coordinates, the zebra crossing boundary coordinates and the inclination angle of the zebra crossing; judging whether the difference value between the pixel gray value in the target crosswalk area and the pixel gray values in other areas outside the target crosswalk area is smaller than a threshold value, if so, verifying to pass, and if not, verifying to fail.
More specifically, after the crosswalk region is identified from the road surface image through fourier transformation, the identified crosswalk region can be further verified through hough transformation, so that the rationality of the identified crosswalk region is verified, namely whether the crosswalk region is identified from the road surface image according to the coordinate range based on fourier transformation is verified to be correctly marked or not. The method specifically comprises the following steps: and (3) performing distortion correction on the boundary image of the identified crosswalk region, longitudinally traversing the whole picture, and extracting pixel coordinates (x, y) of the boundary line of the crosswalk region.
For the crosswalk region, the zebra stripes of the crosswalk region are vertical and inclined, the slope of the zebra stripes is approximately in the range of [60, 90], and in the embodiment of the present application, the image space coordinates (x, y) of the crosswalk region are converted into polar coordinates (ρ, θ), and at this time, a point on the image space coordinates (linear coordinate system x-y) of the crosswalk region corresponds to a curve on the parameter space (polar coordinate system ρ - θ), that is, a sinusoidal curve (ρ=xcos θ+ysin θ).
After the image space coordinates (x, y) of the crosswalk region are converted into polar coordinates (ρ, θ), θ is discretized so that θ takes values of 60, 61, 62, …,90. Calculating rho according to the image space coordinates (x, y) of the pedestrian crossing region and each angle theta, counting the times of similar rho occurrence in the same interval under each angle theta, wherein the image space coordinates (x, y) with the largest occurrence times are marking boundaries of the pedestrian crossing region, calculating the boundary coordinates (such as upper left coordinates and lower right coordinates) of the zebra crossing in the pedestrian crossing region according to the image space coordinates (x, y) of the pedestrian crossing region, the width, length and other attribute characteristics of the zebra crossing, and identifying 4 fixed-point pixel coordinates of the pedestrian crossing region according to the boundary of the pedestrian crossing region, the boundary coordinates of the zebra crossing and the angle of the zebra crossing of the pedestrian crossing region, and redefining the pedestrian crossing region.
After the pedestrian crossing area is redefined by using the Hough transformation, judging whether the pixel gray values in the redefined pedestrian crossing area are approximate or consistent with the pixel gray values of other areas outside the pedestrian crossing area, if so, indicating that the pedestrian crossing area identified based on the Fourier transformation is correctly defined, if the difference value of the pixel gray values exceeds a threshold value, indicating that the pedestrian crossing area identified based on the Fourier transformation is unreasonably defined, re-executing the steps S201 to S205 to re-identify the pedestrian crossing area, and re-verifying the pedestrian crossing area identified in the step S205 by adopting the step S207 until the pedestrian crossing area identified based on the Fourier transformation passes the verification of the step S207.
It should be noted that the steps S201 to S205 have the same or similar implementation manners, and the same points can be referred to each other, which is not repeated here in the embodiments of the present application.
According to the technical scheme provided by the embodiment of the application, the pedestrian crossing area in the pavement can be automatically identified according to the acquired actual pavement video stream, the manual early-stage drawing of the pedestrian crossing area is not needed, time and labor are saved, the pedestrian crossing area can be identified again according to the acquired video stream when the pedestrian crossing is repainted, and the detection accuracy of the pedestrian crossing area is improved. In addition, the accuracy and the rationality of the pedestrian crossing area identified based on the Fourier change are verified based on the Hough transformation, and when the pedestrian crossing area identified based on the Fourier change is unreasonable, the pedestrian crossing area can be identified again, so that the accuracy of detecting the pedestrian crossing area is further improved, and the reliability of detecting the pedestrian crossing area is improved.
As shown in fig. 3, an embodiment of the present application provides a method for identifying a crosswalk area, where an execution subject of the method may be a server, where the server may be an independent server or may be a server cluster formed by a plurality of servers, and the server may be a server capable of identifying a crosswalk area, and the method for identifying a crosswalk area specifically may include the following steps:
in step S301, a video stream of a road surface captured by a traffic road side camera is acquired.
In step S303, a road surface image including a crosswalk region is extracted from the video stream, and the coordinate range of the crosswalk region in the road surface image is marked.
In step S304, median filtering processing is performed on the road surface image.
Specifically, in order to further eliminate impulse noise in the road surface image including the crosswalk region and thereby improve the image quality of the road surface image including the crosswalk region, the road surface image including the crosswalk region may be further extracted from the video stream and subjected to median filtering processing, thereby further eliminating impulse noise in the picture image including the crosswalk region.
In step S305, a crosswalk region is identified from the road surface image based on the coordinate range based on the fourier transform.
Specifically, a crosswalk region is identified from a median-filtered road surface image based on a fourier transform according to a coordinate range.
It should be noted that, the steps S301 to S302 have the same or similar implementation manner as the steps S101 to S102 in the above embodiments, which can be referred to each other, and the embodiments of the present application are not repeated here.
According to the technical scheme provided by the embodiment of the application, the pedestrian crossing area in the pavement can be automatically identified according to the acquired actual pavement video stream, the manual early-stage drawing of the pedestrian crossing area is not needed, time and labor are saved, the pedestrian crossing area can be identified again according to the acquired video stream when the pedestrian crossing is repainted, and the detection accuracy of the pedestrian crossing area is improved. In addition, impulse noise in the road surface image of the crosswalk region is further eliminated, so that the image quality of the road surface image including the crosswalk region is improved, and the accuracy of crosswalk region detection is further improved.
According to the method for identifying a crosswalk area provided in the foregoing embodiment, based on the same technical concept, the embodiment of the present application further provides a device for identifying a crosswalk area, and fig. 4 is a schematic diagram of module components of the device for identifying a crosswalk area provided in the embodiment of the present application, where the device for identifying a crosswalk area is used to execute the method for identifying a crosswalk area described in fig. 1 to 3, as shown in fig. 4, and the device 400 for identifying a crosswalk area includes: the acquisition module 401 is configured to acquire a video stream of a road surface captured by a traffic road side camera; an extracting module 402, configured to extract a pavement image including a crosswalk region from the video stream, and mark a coordinate range of the crosswalk region in the pavement image; an identification module 403 is configured to identify a crosswalk region from the road surface image according to the coordinate range based on fourier transform.
According to the technical scheme provided by the embodiment of the application, the pedestrian crossing area in the pavement can be automatically identified according to the acquired actual pavement video stream, the manual early-stage drawing of the pedestrian crossing area is not needed, time and labor are saved, the pedestrian crossing area can be identified again according to the acquired video stream when the pedestrian crossing is repainted, and the detection accuracy of the pedestrian crossing area is improved.
In a possible implementation manner, the identifying module 403 is further configured to verify whether the identified crosswalk area is marked correctly through hough transformation, and re-identify the crosswalk area if the verification is not passed, and determine that the crosswalk area is marked correctly if the verification is passed.
In a possible implementation manner, the identifying module 403 is further configured to convert the image space pixel coordinates of the boundary line of the identified crosswalk area into polar coordinates, where an angle of the polar coordinates is an inclination angle of the zebra stripes in the identified crosswalk area; calculating the marking boundary coordinates of the identified crosswalk region according to the image space pixel coordinates of the boundary line of the identified crosswalk region and the inclination angle range of the zebra stripes; calculating the zebra crossing boundary coordinates in the identified crosswalk region according to the image space pixel coordinates of the boundary line of the identified crosswalk region and the attribute characteristics of the zebra crossing; redefining a target crosswalk region based on the marking boundary coordinates, the zebra crossing boundary coordinates and the inclination angle of the zebra crossing; judging whether the difference value between the pixel gray value in the target crosswalk area and the pixel gray values in other areas outside the target crosswalk area is smaller than a threshold value, if so, verifying to pass, and if not, verifying to fail.
In a possible implementation manner, the extracting module 402 is further configured to input the video stream to a dark net deep learning model, perform contour recognition on a moving object in the video stream through the dark net deep learning model, and obtain a plurality of images output by the dark net deep learning model; and performing image filling on the plurality of images to obtain a pavement image, wherein the image filling indicates to filter moving objects in the plurality of images and perform color filling on the area after the moving objects are filtered.
In a possible implementation, the identification module 403 is further configured to perform gray-scale processing on the road surface image.
In a possible implementation manner, the identifying module 403 is further configured to perform low-pass filtering on the target image in the coordinate range in the pavement image through fourier transform, perform inverse fourier transform on the target image after the low-pass filtering, and identify the crosswalk area from the target image.
In a possible implementation, the identification module 403 is further configured to perform median filtering processing on the road surface image.
The device for identifying the crosswalk region provided by the embodiment of the application can realize each process in the embodiment corresponding to the method for identifying the crosswalk region, and is not repeated here for avoiding repetition.
It should be noted that, the identification device for the crosswalk area provided by the embodiment of the present application and the identification method for the crosswalk area provided by the embodiment of the present application are based on the same application conception, so that the implementation of the embodiment may refer to the implementation of the identification method for the crosswalk area, and the repetition is omitted.
The embodiment of the application also provides an electronic device for executing the method for identifying the crosswalk area, and fig. 5 is a schematic structural diagram of an electronic device for implementing the embodiments of the application, as shown in fig. 5, based on the same technical concept. The electronic device may vary considerably in configuration or performance and may include one or more processors 501 and memory 502, where the memory 502 may store one or more stored applications or data. Wherein the memory 502 may be transient storage or persistent storage. The application programs stored in memory 502 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for use in an electronic device.
Still further, the processor 501 may be configured to communicate with the memory 502 and execute a series of computer executable instructions in the memory 502 on an electronic device. The electronic device may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input/output interfaces 505, and one or more keyboards 506.
In this embodiment, the electronic device includes a processor, a communication interface, a memory, and a communication bus; the processor, the communication interface and the memory complete communication with each other through a bus; a memory for storing a computer program; the processor is configured to execute the program stored in the memory, implement each step in the above method embodiments, and have the beneficial effects of the above method embodiments, and in order to avoid repetition, the embodiments of the present application are not described herein again.
The embodiment also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps in the above method embodiments, and has the advantages of the above method embodiments, and in order to avoid repetition, the embodiments of the present application are not described herein.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, the electronic device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flashRAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transshipment) such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive carrier, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application should be carried within the scope of the claims of the present application.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210265954.XA CN116805400A (en) | 2022-03-17 | 2022-03-17 | Pedestrian crosswalk area identification method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210265954.XA CN116805400A (en) | 2022-03-17 | 2022-03-17 | Pedestrian crosswalk area identification method and device and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116805400A true CN116805400A (en) | 2023-09-26 |
Family
ID=88079849
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210265954.XA Pending CN116805400A (en) | 2022-03-17 | 2022-03-17 | Pedestrian crosswalk area identification method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116805400A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US12010353B2 (en) | 2010-04-13 | 2024-06-11 | Ge Video Compression, Llc | Inheritance in sample array multitree subdivision |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013114652A (en) * | 2011-12-01 | 2013-06-10 | Yazaki Energy System Corp | Crosswalk detection device |
| US20160055751A1 (en) * | 2014-08-22 | 2016-02-25 | Hyundai Mobis Co., Ltd. | Lane detection apparatus and operating method for the same |
| KR101677875B1 (en) * | 2016-04-12 | 2016-11-18 | 숭실대학교산학협력단 | Apparatus and method for line segment detection |
| KR101823541B1 (en) * | 2017-03-28 | 2018-01-31 | 경북대학교 산학협력단 | Apparatus and method for detecting cross road |
| CN107944407A (en) * | 2017-11-30 | 2018-04-20 | 中山大学 | A kind of crossing zebra stripes recognition methods based on unmanned plane |
| CN108333634A (en) * | 2018-01-29 | 2018-07-27 | 河南工业大学 | A kind of Ground Penetrating Radar buried target localization method based on frequency spectrum remnants conspicuousness detection methods |
| KR101936004B1 (en) * | 2017-10-12 | 2019-01-08 | (주)에이치엠씨 | Intelligent pedestrian detecting method |
| WO2020048487A1 (en) * | 2018-09-05 | 2020-03-12 | 北京嘀嘀无限科技发展有限公司 | Image data processing method and system |
| CN111695374A (en) * | 2019-03-12 | 2020-09-22 | 中国科学院上海高等研究院 | Method, system, medium, and apparatus for segmenting zebra crossing region in monitoring view |
| US20210097308A1 (en) * | 2019-09-30 | 2021-04-01 | Gm Cruise Holdings Llc | Crosswalk detection |
| CN112949626A (en) * | 2021-02-03 | 2021-06-11 | 深圳大学 | Zebra crossing detection method based on HSV color space |
-
2022
- 2022-03-17 CN CN202210265954.XA patent/CN116805400A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013114652A (en) * | 2011-12-01 | 2013-06-10 | Yazaki Energy System Corp | Crosswalk detection device |
| US20160055751A1 (en) * | 2014-08-22 | 2016-02-25 | Hyundai Mobis Co., Ltd. | Lane detection apparatus and operating method for the same |
| KR101677875B1 (en) * | 2016-04-12 | 2016-11-18 | 숭실대학교산학협력단 | Apparatus and method for line segment detection |
| KR101823541B1 (en) * | 2017-03-28 | 2018-01-31 | 경북대학교 산학협력단 | Apparatus and method for detecting cross road |
| KR101936004B1 (en) * | 2017-10-12 | 2019-01-08 | (주)에이치엠씨 | Intelligent pedestrian detecting method |
| CN107944407A (en) * | 2017-11-30 | 2018-04-20 | 中山大学 | A kind of crossing zebra stripes recognition methods based on unmanned plane |
| CN108333634A (en) * | 2018-01-29 | 2018-07-27 | 河南工业大学 | A kind of Ground Penetrating Radar buried target localization method based on frequency spectrum remnants conspicuousness detection methods |
| WO2020048487A1 (en) * | 2018-09-05 | 2020-03-12 | 北京嘀嘀无限科技发展有限公司 | Image data processing method and system |
| CN111695374A (en) * | 2019-03-12 | 2020-09-22 | 中国科学院上海高等研究院 | Method, system, medium, and apparatus for segmenting zebra crossing region in monitoring view |
| US20210097308A1 (en) * | 2019-09-30 | 2021-04-01 | Gm Cruise Holdings Llc | Crosswalk detection |
| CN112949626A (en) * | 2021-02-03 | 2021-06-11 | 深圳大学 | Zebra crossing detection method based on HSV color space |
Non-Patent Citations (2)
| Title |
|---|
| ZHENG-DE ZHANG等: "CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5", NEURAL COMPUTING AND APPLICATIONS, vol. 34, 21 February 2022 (2022-02-21), pages 10719 * |
| 陈宁;洪丰;白冰;: "基于边缘特征和霍夫变换的斑马线识别方法", 浙江科技学院学报, no. 06, 31 December 2019 (2019-12-31) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US12010353B2 (en) | 2010-04-13 | 2024-06-11 | Ge Video Compression, Llc | Inheritance in sample array multitree subdivision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111382704B (en) | Vehicle line pressing violation judging method and device based on deep learning and storage medium | |
| JP6259928B2 (en) | Lane data processing method, apparatus, storage medium and equipment | |
| CN105718872B (en) | Auxiliary method and system for rapidly positioning lanes on two sides and detecting vehicle deflection angle | |
| CN107609555B (en) | License plate detection method, vehicle type identification method applying license plate detection method and related device | |
| CN102354457B (en) | General Hough transformation-based method for detecting position of traffic signal lamp | |
| CN110298300A (en) | A method of detection vehicle violation crimping | |
| CN114387591A (en) | License plate recognition method, system, device and storage medium | |
| CN111027535A (en) | License plate recognition method and related equipment | |
| CN111160132B (en) | Method and device for determining lane where obstacle is located, electronic equipment and storage medium | |
| CN109583393B (en) | Lane line end point identification method and device, equipment and medium | |
| CN113505638B (en) | Method and device for monitoring traffic flow and computer readable storage medium | |
| WO2021088504A1 (en) | Road junction detection method and apparatus, neural network training method and apparatus, intelligent driving method and apparatus, and device | |
| Zhang et al. | Image-based approach for parking-spot detection with occlusion handling | |
| CN112699825A (en) | Lane line identification method and device | |
| CN114724119A (en) | Lane line extraction method, lane line detection apparatus, and storage medium | |
| CN112597960B (en) | Image processing methods, processing devices and computer-readable storage media | |
| Chen | RETRACTED ARTICLE: Road vehicle recognition algorithm in safety assistant driving based on artificial intelligence | |
| CN116381698B (en) | Road remains detection method and device and electronic equipment | |
| CN113643374B (en) | Multi-camera calibration method, device, equipment and medium based on road features | |
| CN113435350A (en) | Traffic marking detection method, device, equipment and medium | |
| CN116805400A (en) | Pedestrian crosswalk area identification method and device and electronic equipment | |
| CN116129383B (en) | Method and device for detecting drivable area, electronic equipment and storage medium | |
| Hsieh et al. | A real-time mobile vehicle license plate detection and recognition for vehicle monitoring and management | |
| CN108389177B (en) | A kind of vehicle bumper damage detection method and traffic safety early warning method | |
| CN119314120A (en) | Vehicle tracking and re-identification method, device, electronic device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |