CN108876817A - Cross track analysis method and device, electronic equipment and storage medium - Google Patents

Abstract

The present disclosure relates to a cross track analysis method and device, electronic equipment and a storage medium. The method includes: determining a target image of a target object in an image to be analyzed, where the image to be analyzed includes time information and location information; according to the The time information and location information of the image to be analyzed are determined to determine the time information and location information of the target image; the trajectory of the target object is obtained according to the time information and location information of the target image; Obtain the intersection trajectories of multiple target objects. In the embodiment of the present disclosure, the cross trajectory obtained by using the target images of each target object has high precision and accurate positioning.

Description

Cross track analysis method and device, electronic equipment and storage medium

technical field

The present disclosure relates to the field of information technology, and in particular, to a method and device for cross-trajectory analysis, electronic equipment, and a storage medium.

Background technique

With the development of information technology, there are more and more demands to determine the cross-trajectories between different objects. For example, in security management departments such as public security, using cross-trajectories between different suspects to deploy and control suspects can improve the success rate of arrest. In the traditional cross-trajectory analysis method, the method of determining the cross-trajectories between different suspects after using the wireless communication equipment carried by the suspect to track the suspect is limited by the influence of the coverage quality of the wireless network signal and the It is limited to the situation where the suspect avoids using the wireless communication device, resulting in a low accuracy rate of the determined crossing trajectory.

Contents of the invention

In view of this, the present disclosure proposes a cross-trajectory analysis technical solution.

According to an aspect of the present disclosure, a cross-trajectory analysis method is provided, the method comprising:

Determining a target image of a target object in an image to be analyzed, where the image to be analyzed includes time information and location information;

determining the time information and location information of the target image according to the time information and location information of the image to be analyzed;

Obtaining the trajectory of the target object according to the time information and location information of the target image;

The intersecting trajectories of the multiple target objects are obtained according to the trajectories of the multiple target objects.

In a possible implementation manner, determining the target image of the target object in the image to be analyzed includes:

detecting a target object in the image to be analyzed;

When the target object is detected in the image to be analyzed, a target image of the target object is determined according to the image to be analyzed.

In a possible implementation manner, determining the target image of the target object in the image to be analyzed includes:

Detect candidate target objects in the original image;

Intercepting images including each of the candidate target objects from the original image to generate an image to be analyzed;

A target image of a target object is determined in the image to be analyzed.

In a possible implementation manner, determining the target image of the target object in the image to be analyzed includes:

A target image of a target object is determined in the image to be analyzed according to the similarity between the target object and the candidate target object.

In a possible implementation manner, determining the target image of the target object in the image to be analyzed includes:

determining the target object in any of the reference images for the target object;

According to the target object in each reference image, the target image of the target object in each reference image is determined in the image to be analyzed.

In a possible implementation manner, obtaining the track of the target object according to time information and location information of the target image includes:

According to the time information and location information of the target image of the target object in each reference image, determine the trajectory to be merged corresponding to the target object in each reference image;

The trajectories to be combined corresponding to the target objects in each reference image are combined to obtain the trajectories of the target objects.

In a possible implementation manner, the trajectories to be combined corresponding to the target objects in each reference image are combined to obtain the trajectories of the target objects, including:

Merge the trajectories to be merged corresponding to the target objects in each reference image to obtain the merged trajectories;

The abnormal time information and/or abnormal location information in the merged trajectory is removed to obtain the trajectory of the target object.

According to an aspect of the present disclosure, a cross-trajectory analysis device is provided, the device comprising:

A target image determination module, used to determine the target image of the target object in the image to be analyzed, the image to be analyzed includes time information and location information;

an information determination module, configured to determine the time information and location information of the target image;

A target trajectory determination module, configured to obtain the trajectory of the target object according to the time information and location information of the target image;

The intersecting trajectory determination module is configured to obtain the intersecting trajectories of the plurality of target objects according to the trajectories of the plurality of target objects.

In a possible implementation manner, the target image determination module includes:

A detection submodule, configured to detect a target object in the image to be analyzed;

The first target image determining submodule is configured to determine a target image of the target object according to the image to be analyzed when the target object is detected in the image to be analyzed.

In a possible implementation manner, the target image determination module includes:

A candidate target object detection sub-module is used to detect candidate target objects in the original image;

The image-to-be-analyzed generating submodule is configured to intercept images including each of the candidate target objects from the original image, and generate an image to be analyzed;

The second target image determination submodule is used to determine the target image of the target object in the image to be analyzed.

In a possible implementation manner, the second target image determining submodule includes:

A target image determining unit, configured to determine a target image of a target object in the image to be analyzed according to the similarity between the target object and the candidate target object.

In a possible implementation manner, the target image determination module includes:

A target object determining submodule, configured to determine the target object in any reference image for the target object;

The third target image determination sub-module is configured to determine the target image of the target object in each reference image in the image to be analyzed according to the target object in each reference image.

In a possible implementation manner, the target trajectory determination module includes:

The trajectory to be merged determination sub-module is used to determine the trajectory to be merged corresponding to the target object in each reference image according to the time information and location information of the target image of the target object in each reference image;

The first target track determination sub-module is used to combine the track to be combined corresponding to the target object in each reference image to obtain the track of the target object.

In a possible implementation manner, the first target trajectory determination submodule includes:

The merged trajectory determination sub-module is used to merge the to-be-merged trajectories corresponding to the target object in each reference image to obtain the merged trajectory;

The second target trajectory determination submodule is configured to remove abnormal time information and/or abnormal location information in the merged trajectory to obtain the trajectory of the target object.

According to an aspect of the present disclosure, an electronic device is provided, including:

processor;

memory for storing processor-executable instructions;

Wherein, the processor executes the above cross-track analysis method by directly or indirectly invoking the executable instruction.

According to one aspect of the present disclosure, there is provided a computer-readable storage medium, on which computer program instructions are stored, and the computer program instructions are executed by a processor to execute the above-mentioned method for cross-track analysis.

According to the embodiments of the present disclosure, the target image of the target object can be determined in the image to be analyzed, the trajectory of each target object can be obtained according to the target image of each target object, and the cross trajectory of multiple target objects can be obtained by using the trajectories of multiple target objects. The cross trajectory obtained by using the target image of each target object has high precision and accurate positioning.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the specification, serve to explain the principles of the disclosure.

Fig. 1 is a flow chart of a method for cross-track analysis according to an exemplary embodiment;

Fig. 2 is a schematic diagram of a trajectory in a cross-track analysis method shown according to an exemplary embodiment;

Fig. 3 is a flowchart of a method for analyzing a cross track according to an exemplary embodiment;

Fig. 4 is a flow chart of step S10 in a method for cross-trajectory analysis according to an exemplary embodiment;

Fig. 5 is a flow chart of step S10 in a method for cross-trajectory analysis according to an exemplary embodiment;

Fig. 6 is a flow chart of step S10 in a method for cross-trajectory analysis according to an exemplary embodiment;

Fig. 7 is a flow chart of step S30 in a method for cross-trajectory analysis according to an exemplary embodiment;

Fig. 8 is a block diagram of a cross track analysis device according to an exemplary embodiment;

Fig. 9 is a block diagram of a cross track analysis device according to an exemplary embodiment;

Fig. 10 is a block diagram of an electronic device according to an exemplary embodiment.

Detailed ways

Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in the figures indicate functionally identical or similar elements. While various aspects of the embodiments are shown in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as superior or better than other embodiments.

In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific implementation manners. It will be understood by those skilled in the art that the present disclosure may be practiced without some of the specific details. In some instances, methods, means, components and circuits that are well known to those skilled in the art have not been described in detail so as to obscure the gist of the present disclosure.

Fig. 1 is a flow chart of a cross-track analysis method according to an exemplary embodiment. As shown in Fig. 1, the cross-track analysis method includes:

Step S10, determining the target image of the target object in the image to be analyzed, where the image to be analyzed includes time information and location information.

In a possible implementation manner, the target object may include various types of objects such as people, animals, plants, and buildings. The type of target object can be determined according to requirements. Target objects can include one or more types of objects. For example, the target object may be character A, or a combination of character A plus animal B.

The image to be analyzed may include a single image or multiple images. The image to be analyzed may be a static image, or multiple frame images in a video stream. The image to be analyzed may include images of multiple objects or an image of a single object.

The image to be analyzed includes time information and location information when the image to be analyzed is taken. For example, for an image to be analyzed captured by a surveillance camera, the time information of the image to be analyzed may be determined according to the shooting time, and the location information of the image to be analyzed may be determined according to the installation location information of the surveillance camera. The location information may include latitude and longitude information, and may also include postal address information.

The target image of the target object can be determined in the image to be analyzed according to information such as the image or name of the target object.

The image to be analyzed including the image of the target object may be determined as the target image of the target object. An image of the target object may also be intercepted from the image to be analyzed as a target image of the target object.

When the image to be analyzed is a frame image in a video stream, multiple target images of the target object may be determined in the image to be analyzed.

Step S20: Determine the time information and location information of the target image according to the time information and location information of the image to be analyzed.

In a possible implementation manner, when the image to be analyzed including the target object is determined as the target image of the target object, the time information and location information of the image to be analyzed may be determined as the time information and location information of the target image.

When an image including the target object is intercepted from the image to be analyzed as the target image, time information and location information of the image to be analyzed before interception may be determined as time information and location information of the target image.

Step S30, obtaining the track of the target object according to the time information and location information of the target image.

In a possible implementation manner, multiple target images of the target object may be determined in the image to be analyzed. According to the time information and location information of each target image, the target images can be connected in sequence according to the time sequence, and the correlation relationship between the location information of each target image can be obtained, and the correlation relationship between the location information of each target image can be obtained. Determine the trajectory of the target object.

Each target image can be marked on the map according to the location information of each target image, and then according to the time information of each target image, each target image can be connected in sequence according to time, and the linear trajectory of the target object can be obtained on the map . The trajectory of the target object displayed on the map is more intuitive.

Fig. 2 is a schematic diagram of merging trajectories in a cross-trajectory analysis method shown according to an exemplary embodiment. As shown in Fig. 2 , five points in the figure are five target images of the target object, and the five target image images are divided into After the time sequence is connected sequentially, the linear trajectory of the target image can be obtained on the map.

When there is only one target image of the target object, the track of the target object is a location including time information.

Step S40, obtaining cross trajectories of multiple target objects according to the trajectories of multiple target objects.

In a possible implementation manner, when there are multiple target objects, the trajectories of the overlapping parts of the trajectories of the target objects may be determined as cross trajectories of the multiple target objects.

The cross track may be a track determined according to the overlapped target images after determining the target images whose time information and location information overlap in all target images of multiple target objects. The coincidence of time information may include that the time difference calculated according to the time information is within a time difference threshold range, and the coincidence of location information may include that the distance calculated according to the location information is within a distance threshold range. For example, the cross trajectory of the target object 1 and the target object 2 is a trajectory in which the target object 1 and the target object 2 appear at the same place at the same time.

The intersecting trajectory may be a trajectory determined according to the overlapping target images after determining the target images whose location information overlaps among all the target images of the multiple target objects. For example, the intersecting trajectory of the target object 3 and the target object 4 is a trajectory in which the target object 3 and the target object 4 appeared at the same place at the same or different times.

The intersecting trajectory may be an intersecting trajectory determined from intersection points of trajectories of a plurality of target objects. For example, the trajectory of the target object 5 and the trajectory of the target object 6 do not coincide in time and place, but the two trajectories of the target object that 5 and the target object 6 include one or more intersection points. The intersection trajectory of the target object 5 and the target object 6 can be determined from the intersection point. Wherein, the intersection point is not a point on the trajectories of the target object 5 and the target object 6 .

In this embodiment, the target image of the target object can be determined in the image to be analyzed, the trajectories of each target object can be obtained according to the target images of each target object, and the cross trajectories of multiple target objects can be obtained by using the trajectories of multiple target objects. The cross trajectory obtained by using the target image of each target object has high precision and accurate positioning.

Fig. 3 is a flow chart of a cross-track analysis method shown according to an exemplary embodiment. As shown in Fig. 3, step S10 in the cross-track analysis method includes:

Step S11, detecting a target object in the image to be analyzed.

In a possible implementation manner, the target object may be detected in the image to be detected according to an image of the target object, for example, a photo or a portrait of the target object may be used. The target image of the target object can be determined in the image to be analyzed by using technologies such as image recognition. It is also possible to input the image of the target object into the neural network, and determine the image of the target object in the image to be analyzed according to the output result of the neural network.

It is also possible to detect a cat in the image to be analyzed according to information such as the name of the target object, for example, according to the name "cat" of the target object. The target image of the target object can be determined in the image to be analyzed by using technologies such as image recognition. It is also possible to input the name of the target object into the neural network, and determine the image of the target object in the image to be analyzed according to the output result of the neural network.

Step S12, when the target object is detected in the image to be analyzed, determine a target image of the target object according to the image to be analyzed.

In a possible implementation manner, the image to be analyzed may only include the image of the target object. For example, image A to be analyzed only includes object 1, and image B to be analyzed only includes object 2. When the target object is object 1, the object 1 can be detected in the image A to be analyzed, and then the image A to be analyzed can be determined as the target image of the object 1.

The target object and other object pair images may also be included in the image to be analyzed. For example, image A to be analyzed includes three objects, which are object 1, object 2, and object 3, respectively. The target object is object 1, and the object 1 can be identified in the image A to be analyzed according to the image of the object 1, and the image A to be analyzed can be determined as the target image of the object 1. It is also possible to intercept the image of the object 1 in the image A to be analyzed as the target image of the target object.

In this embodiment, when the target object is detected in the image to be analyzed, a target image of the target object is determined according to the image to be analyzed. The accurate target image can be obtained according to the detection result of the image to be analyzed, so that the determination result of the cross track is more accurate and reliable.

Fig. 4 is a flow chart of step S10 in a cross-track analysis method according to an exemplary embodiment. As shown in Fig. 4, step S10 in the cross-track analysis method includes:

Step S13, detecting candidate target objects in the original image.

In one possible implementation, the original image may include an unprocessed image. For example, the original image may be a frame image in a video stream captured by a surveillance camera. Multiple candidate target objects may be included in the original image. For example, in the original image captured by a surveillance camera in a crowded place, images of multiple people may be included.

Candidate target objects can be detected in raw images using technologies such as face recognition. The original image can be input into the face recognition neural network, and each candidate target object in the original image can be detected according to the output result of the face recognition neural network. Each candidate target object may be identified in the original image according to the detection result, for example, the detection frame of each candidate target object may be identified in the original image.

Step S14, intercepting images including each of the candidate target objects from the original image to generate an image to be analyzed.

In a possible implementation manner, an image including the candidate target object may be intercepted from the original image according to the detection result of each candidate target object. For example, three candidate target objects are detected in the original image D, which are candidate target object 1, candidate target object 2 and candidate target object 3, respectively. Candidate target object 1, candidate target object 2 and candidate target object For the image corresponding to the target object 3, a total of three images to be analyzed are obtained.

Step S15, determining a target image of the target object in the image to be analyzed.

In a possible implementation manner, an image to be analyzed including multiple candidate target objects may be generated according to the original image, and then a target object of the target object is determined according to the image to be analyzed including the multiple candidate target objects.

According to the needs, according to the original image of the selected time and/or selected location, the image to be analyzed can be generated, the image library to be analyzed can be established, and the target image of the target object can be determined according to the established image library to be analyzed, which can be more targeted The trajectory of the target object can be determined, and the cross trajectory of multiple target objects can be obtained more pertinently.

In this embodiment, after the candidate target objects are detected in the original image, an image including each of the candidate target objects is intercepted to generate an image to be analyzed, and the target image of the target object is determined in the image to be analyzed, because in the image to be analyzed Including only a single candidate target object makes the determination process of the target image more efficient and accurate.

Fig. 5 is a flow chart of step S10 in a cross-track analysis method according to an exemplary embodiment. As shown in Fig. 5, step S15 in the cross-track analysis method includes:

Step S151, according to the similarity between the target object and the candidate target object, determine the target image of the target object in the image to be analyzed.

In a possible implementation manner, the target object may be detected in the image to be analyzed according to the image of the target object. When the similarity between the target object and the candidate target object in the image to be analyzed is greater than a similarity threshold, the image to be analyzed may be determined as the target image of the target object.

The specified features can be set according to requirements, the specified features of the target object can be compared with the specified features of the candidate target object, and the similarity between the target object and the candidate target object can be determined according to the comparison result. For example, if the target object is a person, the characteristics of the nose and hair of the target object can be compared with the characteristics of the nose and hair of the candidate target object. An image to be analyzed of a candidate target object whose hair is curly hair is a target image of the target object.

The feature value of the target object and the feature value of the candidate target object can be extracted according to requirements, and the similarity between the target object and the candidate target object can be determined by calculating the difference between the feature value of the target object and the feature value of the candidate target object.

It is also possible to obtain the first similarity between the target object and the third object, and obtain the second similarity between the candidate target object and the same third object, and then determine the target according to the first similarity and the second similarity The similarity between the object and the candidate target object.

In this embodiment, the target image determined according to the similarity between the target object and the candidate target object is more accurate. The corresponding relationship between the similarity degree and the target image can be adjusted according to requirements, and the scope of application of this embodiment is wider.

Fig. 6 is a flow chart of step S10 in a cross-track analysis method according to an exemplary embodiment. As shown in Fig. 6, step S10 in the cross-track analysis method includes:

Step S16, determining the target object in any reference image for the target object.

In a possible implementation manner, the reference image of the target object may be a photo or portrait including the target object. Due to different shooting angles and shooting environments, the clarity and characteristics of the target object in the image will be different, and the target object in a single image may not fully reflect all the features of the target object. Objects of interest can be determined in multiple reference images.

The target object determined in any reference image may include different features of the target object. For example, the target object is a person, the face of the target object detected in the reference image 1 is a side face, and the target object detected in the reference image 2 is in a state of bowing its head. Only based on the reference image 1 or the reference image 2, all the features of the target object cannot be fully reflected, and the most accurate target image cannot be obtained.

Step S17, according to the target object in each reference image, determine the target image of the target object in each reference image in the image to be analyzed.

In a possible implementation manner, according to the target object determined in any reference image, the target image corresponding to the reference image may be determined in the image to be analyzed. According to the target object determined in each reference image, the target image corresponding to the target object in each reference image can be determined in the image to be analyzed.

The trajectory corresponding to each reference image can be obtained according to the time information and location information of the target image corresponding to each reference image. Then, the trajectory of the target object is obtained according to the trajectory corresponding to each reference image.

The track of the target object can also be obtained directly according to the time information and location information of the target image corresponding to all the reference images.

In this embodiment, the target object is determined according to any reference image of the target object, and then the target image is determined in the image to be analyzed according to the target object in each reference image. According to different reference images, more comprehensive features of each target object can be obtained, more accurate trajectories of each target object can be obtained, and the merged trajectories of each target object can also be made more accurate.

Fig. 7 is a flow chart of step S30 in a cross-track analysis method according to an exemplary embodiment. As shown in Fig. 7, step S30 in the cross-track analysis method includes:

Step S31, according to the time information and location information of the target image of the target object in each reference image, determine the track to be merged corresponding to the target object in each reference image.

In a possible implementation manner, according to the target images determined in each reference image, the trajectory of the target object corresponding to each reference image may be determined in the image to be recognized. For example, target object A has three reference images, namely reference image 1, reference image 2 and reference image 3. According to the target object A determined in the reference image 1, the track 1 of the target object A to be merged can be determined in the image to be analyzed; according to the target object A determined in the reference image 2, the target object A can be determined in the image to be analyzed The trajectory 2 to be merged; according to the target object A determined in the reference image 3, the trajectory 3 to be merged of the target object A can be determined in the image to be analyzed.

Step S32, merging the trajectories to be combined corresponding to the target objects in each reference image to obtain the trajectories of the target objects.

In a possible implementation manner, the trajectories to be combined corresponding to the reference images may be combined according to requirements to obtain the trajectories of the target object. For example, merging tracks to be combined within a specific time period, and/or merging tracks to be combined within a specific geographical range.

In this embodiment, after the trajectories to be merged corresponding to each reference image are obtained according to the target images corresponding to each reference image, the trajectories of the target object can be determined according to the trajectories to be combined of each reference image. According to the track to be combined of each reference image, the track of the target object can be made more accurate and reliable.

In a possible implementation manner, step S32 in the cross track analysis method includes:

The trajectories to be merged corresponding to the target objects in each reference image are merged to obtain the merged trajectories.

The abnormal time information and/or abnormal location information in the merged trajectory is removed to obtain the trajectory of the target object.

In a possible implementation manner, the merging track may be obtained by merging the trajectories to be combined of each reference image, or may be obtained by merging the trajectories to be combined of part of the reference images. For example, when the track to be combined of one of the reference images deviates significantly from the tracks to be combined of other reference images, it can be excluded and the track to be combined of the remaining reference images can be used to obtain the combined track.

In a possible implementation manner, abnormal time information and/or abnormal location information in the merged trajectory may be removed according to requirements. The average time corresponding to each target image can be calculated according to the time information, and the point where the target image with the largest time difference from the average time is removed to obtain the trajectory of the target object. It is also possible to remove the points where the target image is beyond the set geographic range according to the location information, so as to obtain the track of the target object.

In this embodiment, the track of the target object can be obtained by removing the abnormal time information and the abnormal location information in the merged track according to requirements. The trajectory of each target object can be made more accurate, and the cross trajectory of each target object can also be made more accurate.

Fig. 8 is a block diagram of a cross track analysis device according to an exemplary embodiment. As shown in Fig. 8, the cross track analysis device includes:

The target image determining module 10 is used to determine the target image of the target object in the image to be analyzed, and the image to be analyzed includes time information and location information.

The information determining module 20 is configured to determine the time information and location information of the target image.

A target trajectory determining module 30, configured to obtain the trajectory of the target object according to the time information and location information of the target image.

The intersecting trajectory determination module 40 is configured to obtain the intersecting trajectories of the plurality of target objects according to the trajectories of the plurality of target objects.

Fig. 9 is a block diagram of a cross track analysis device according to an exemplary embodiment. As shown in Fig. 9, in a possible implementation manner, the target image determination module 10 includes:

A detection submodule 11, configured to detect a target object in the image to be analyzed;

The first target image determining sub-module 12 is configured to determine a target image of the target object according to the image to be analyzed when the target object is detected in the image to be analyzed.

In a possible implementation manner, the target image determination module 10 includes:

The candidate target object detection submodule 13 is used to detect the candidate target object in the original image;

The image-to-be-analyzed generating submodule 14 is configured to intercept images including each of the candidate target objects in the original image to generate an image to be analyzed;

The second target image determining sub-module 15 is configured to determine the target image of the target object in the image to be analyzed.

In a possible implementation manner, the second target image determining submodule 15 includes:

A target image determining unit, configured to determine a target image of a target object in the image to be analyzed according to the similarity between the target object and the candidate target object.

In a possible implementation manner, the target image determination module 10 includes:

The target object determination submodule 16 is used to determine the target object in any reference image for the target object;

The third target image determining sub-module 17 is configured to determine the target image of the target object in each reference image in the image to be analyzed according to the target object in each reference image.

In a possible implementation, the target trajectory determination module 30 includes:

The track to be merged determination submodule 31 is used to determine the track to be merged corresponding to the target object in each reference image according to the time information and location information of the target image of the target object in each reference image;

The first target track determination sub-module 32 is configured to combine the track to be combined corresponding to the target object in each reference image to obtain the track of the target object.

In a possible implementation manner, the first target trajectory determination submodule 32 includes:

A merged trajectory determination unit is used to merge the trajectories to be merged corresponding to the target object in each reference image to obtain the merged trajectory;

The second target trajectory determining unit is configured to remove abnormal time information and/or abnormal location information in the merged trajectory to obtain the trajectory of the target object.

Fig. 10 is a block diagram of an electronic device according to an exemplary embodiment. For example, electronic equipment may be provided as a terminal, a server, or other forms of equipment. The electronic device includes a cross-trajectory analyzing device 1900 . Referring to FIG. 10 , device 1900 includes processing component 1922 , which further includes one or more processors, and a memory resource represented by memory 1932 for storing instructions executable by processing component 1922 , such as application programs. The application programs stored in memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

Device 1900 may also include a power component 1926 configured to perform power management of device 1900 , a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input-output (I/O) interface 1958 . The device 1900 can operate based on an operating system stored in the memory 1932, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™ or the like.

In an exemplary embodiment, there is also provided a computer-readable storage medium, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the device 1900 to implement the above-mentioned method.

The present disclosure can be a system, method and/or computer program product. A computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.

A computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, such as a printer with instructions stored thereon A hole card or a raised structure in a groove, and any suitable combination of the above. As used herein, computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.

Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or a network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .

Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or Source or object code written in any combination, including object-oriented programming languages—such as Smalltalk, C++, etc., and conventional procedural programming languages—such as the “C” language or similar programming languages. Computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement. In cases involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as via the Internet using an Internet service provider). connect). In some embodiments, an electronic circuit, such as a programmable logic circuit, field programmable gate array (FPGA), or programmable logic array (PLA), can be customized by utilizing state information of computer-readable program instructions, which can Various aspects of the present disclosure are implemented by executing computer readable program instructions.

Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It should be understood that each block of the flowcharts and/or block diagrams, and combinations of blocks in the flowcharts and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that when executed by the processor of the computer or other programmable data processing apparatus , producing an apparatus for realizing the functions/actions specified in one or more blocks in the flowchart and/or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause computers, programmable data processing devices and/or other devices to work in a specific way, so that the computer-readable medium storing instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks in flowcharts and/or block diagrams.

It is also possible to load computer-readable program instructions into a computer, other programmable data processing device, or other equipment, so that a series of operational steps are performed on the computer, other programmable data processing device, or other equipment to produce a computer-implemented process , so that instructions executed on computers, other programmable data processing devices, or other devices implement the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, a portion of a program segment, or an instruction that includes one or more Executable instructions. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions.

Having described various embodiments of the present disclosure above, the foregoing description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and alterations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles of the various embodiments, practical applications or technical improvements over technologies in the market, or to enable other persons of ordinary skill in the art to understand the various embodiments disclosed herein.

Claims (10)

1. A cross-track analysis method, characterized in that the method comprises: Determining a target image of a target object in an image to be analyzed, where the image to be analyzed includes time information and location information; determining the time information and location information of the target image according to the time information and location information of the image to be analyzed; Obtaining the trajectory of the target object according to the time information and location information of the target image; The intersecting trajectories of the multiple target objects are obtained according to the trajectories of the multiple target objects. 2. The method according to claim 1, wherein determining the target image of the target object in the image to be analyzed comprises: detecting a target object in the image to be analyzed; When the target object is detected in the image to be analyzed, a target image of the target object is determined according to the image to be analyzed. 3. The method according to claim 1, wherein determining the target image of the target object in the image to be analyzed comprises: Detect candidate target objects in the original image; Intercepting images including each of the candidate target objects from the original image to generate an image to be analyzed; A target image of a target object is determined in the image to be analyzed. 4. The method according to claim 3, wherein determining the target image of the target object in the image to be analyzed comprises: A target image of a target object is determined in the image to be analyzed according to the similarity between the target object and the candidate target object. 5. A cross track analysis device, characterized in that said device comprises: A target image determination module, used to determine the target image of the target object in the image to be analyzed, the image to be analyzed includes time information and location information; an information determination module, configured to determine the time information and location information of the target image; A target trajectory determination module, configured to obtain the trajectory of the target object according to the time information and location information of the target image; The intersecting trajectory determination module is configured to obtain the intersecting trajectories of the plurality of target objects according to the trajectories of the plurality of target objects. 6. The device according to claim 5, wherein the target image determination module comprises: A detection submodule, configured to detect a target object in the image to be analyzed; The first target image determining submodule is configured to determine a target image of the target object according to the image to be analyzed when the target object is detected in the image to be analyzed. 7. The device according to claim 5, wherein the target image determination module comprises: A candidate target object detection sub-module is used to detect candidate target objects in the original image; The image-to-be-analyzed generating submodule is configured to intercept images including each of the candidate target objects from the original image, and generate an image to be analyzed; The second target image determination submodule is used to determine the target image of the target object in the image to be analyzed. 8. The device according to claim 7, wherein the second target image determination submodule comprises: A target image determining unit, configured to determine a target image of a target object in the image to be analyzed according to the similarity between the target object and the candidate target object. 9. An electronic device, characterized in that it comprises: processor; memory for storing processor-executable instructions; Wherein, the processor is configured to: execute the method described in any one of claims 1-4. 10. A computer-readable storage medium, on which computer program instructions are stored, wherein the method according to any one of claims 1 to 4 is implemented when the computer program instructions are executed by a processor.