Tracking vehicle identification method, system, equipment and medium

Abstract

The invention discloses a tracking vehicle identification method, system and device and a medium, and relates to the technical field of computer vision, and the method comprises the steps: determining a first short-time driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of a target vehicle, and determining a second short-time driving behavior semantic sequence of the target vehicle; and calculating the similarity between the first short-time driving behavior semantic sequence and the second short-time driving behavior semantic sequence, and when the similarity exceeds a threshold value, determining the suspected tracking vehicle as a tracking vehicle. According to the method, the tracking vehicle is identified by calculating the similarity between the short-time driving behavior semantic sequence of the vehicle (suspected tracking vehicle) around the vehicle owner and the short-time driving behavior semantic sequence of the vehicle owner (target vehicle), so that the accuracy and timeliness of identifying the tracking vehicle are greatly improved.

Description

Background Art

[0002] In daily work and life, cars have gradually become an important means of transportation for people. However, while cars bring great convenience to people, some lawless elements have also taken advantage of them. Specifically, they maliciously track target persons who are also driving cars by driving cars, thereby infringing on the target person's privacy and even threatening the target person's personal safety.

[0003] Therefore, how to timely detect the vehicle following behind in the process of driving is of great significance to protecting the privacy of the car owner and improving the personal safety of the car owner.

[0004] The traditional method is mainly based on video information, and tracking judgment is performed by judging whether the driving path of the vehicle behind the owner coincides with the owner. However, there is a difficult problem to be solved, that is, the position of the vehicle tracking the owner is not necessarily behind, and there may be more than one vehicle tracking, which brings great difficulties to tracking and identification. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the prior art and specifically provide a method, system, device and medium for tracking vehicle identification, as follows:

[0006] 1) In the first aspect, the present invention provides a method for identifying a tracked vehicle, and the specific technical solution is as follows:

[0007] Determine a first short-term driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of ​​the target vehicle and determine a second short-term driving behavior semantic sequence of the target vehicle, wherein the first short-term driving behavior semantic sequence represents driving behavior characteristics of the suspected tracking vehicle within a preset period, and the second short-term driving behavior semantic sequence represents driving behavior characteristics of the target vehicle within a preset period;

[0008] The similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence is calculated, and when the similarity exceeds a threshold, the suspected tracking vehicle is determined as a tracking vehicle.

[0009] The beneficial effects of the vehicle tracking identification method provided by the present invention are as follows:

[0010] The tracking vehicle is identified by calculating the similarity between the short-term driving behavior semantic sequence of vehicles around the owner (suspected tracking vehicle) and the short-term driving behavior semantic sequence of the owner (target vehicle), greatly improving the accuracy and timeliness of tracking vehicle identification.

[0011] Based on the above solution, the present invention can also be improved as follows.

[0012] Furthermore, the similarity is determined as follows:

[0013] Determine, according to the first short-term driving behavior semantic sequence, a first driving behavior type difference of a corresponding short-term driving behavior semantic sequence, wherein the first driving behavior type difference represents a proportion of the duration of different driving behavior types in the first short-term driving behavior semantic sequence;

[0014] Determine a second driving behavior type difference of a corresponding short-term driving behavior semantic sequence according to the second short-term driving behavior semantic sequence, wherein the second driving behavior type difference represents a time proportion of different driving behavior types in the second short-term driving behavior semantic sequence;

[0015] The similarity is determined based on the first driving behavior type difference and the second driving behavior type difference.

[0016] Further, the first driving behavior type difference / the second driving behavior type difference is determined in the following manner:

[0017] Determine a first same driving behavior type difference and a first different driving behavior type difference of a first short-term driving behavior semantic sequence, and average the first same driving behavior type difference and the first different driving behavior type difference to obtain the first driving behavior type difference;

[0018] Alternatively, a second identical driving behavior type difference and a second different driving behavior type difference of the second short-term driving behavior semantic sequence are determined, and the second identical driving behavior type difference and the second different driving behavior type difference are averaged to obtain the second driving behavior type difference.

[0019] Further, the first same driving behavior type difference or the second same driving behavior type difference is determined in the following manner:

[0020] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, determining a first driving behavior in any equally divided window and a corresponding duration, and determining the first identical driving behavior type difference based on a first corresponding relationship among the semantic label, the first driving behavior, the duration corresponding to the first driving behavior, and the behavior type difference;

[0021] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, the first driving behavior in any equally divided window and the corresponding duration are determined, and based on the first corresponding relationship between the semantic label, the first driving behavior, the duration corresponding to the first driving behavior and the behavior type difference, the second same driving behavior type difference is determined.

[0022] Further, the first different driving behavior type difference or the second different driving behavior type difference is determined in the following manner:

[0023] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, selecting at least one equally divided window that does not contain a specified type from all the semantic labels, and determining the first different driving behavior type difference based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and a second corresponding relationship between behavior types;

[0024] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, and at least one equally divided window that does not contain the specified type is selected from all the semantic labels, and based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and the second corresponding relationship between the behavior types, the second different driving behavior type difference is determined.

[0025] 2) In the second aspect, the present invention also provides a vehicle tracking identification system, and the specific technical solution is as follows:

[0026] The determination module is used to: determine a first short-term driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of ​​the target vehicle and determine a second short-term driving behavior semantic sequence of the target vehicle, wherein the first short-term driving behavior semantic sequence represents the driving behavior characteristics of the suspected tracking vehicle within a preset period, and the second short-term driving behavior semantic sequence represents the driving behavior characteristics of the target vehicle within a preset period;

[0027] The calculation module is used to calculate the similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence, and when the similarity exceeds a threshold, determine the suspected tracking vehicle as a tracking vehicle.

[0028] Based on the above solution, the present invention can also be improved as follows.

[0029] Furthermore, the similarity is determined as follows:

[0030] Determine, according to the first short-term driving behavior semantic sequence, a first driving behavior type difference of a corresponding short-term driving behavior semantic sequence, wherein the first driving behavior type difference represents a proportion of the duration of different driving behavior types in the first short-term driving behavior semantic sequence;

[0031] Determine a second driving behavior type difference of a corresponding short-term driving behavior semantic sequence according to the second short-term driving behavior semantic sequence, wherein the second driving behavior type difference represents a time proportion of different driving behavior types in the second short-term driving behavior semantic sequence;

[0032] The similarity is determined based on the first driving behavior type difference and the second driving behavior type difference.

[0033] Further, the first driving behavior type difference / the second driving behavior type difference is determined in the following manner:

[0034] Determine a first same driving behavior type difference and a first different driving behavior type difference of a first short-term driving behavior semantic sequence, and average the first same driving behavior type difference and the first different driving behavior type difference to obtain the first driving behavior type difference;

[0035] Alternatively, a second identical driving behavior type difference and a second different driving behavior type difference of the second short-term driving behavior semantic sequence are determined, and the second identical driving behavior type difference and the second different driving behavior type difference are averaged to obtain the second driving behavior type difference.

[0036] Further, the first same driving behavior type difference or the second same driving behavior type difference is determined in the following manner:

[0037] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, determining a first driving behavior in any equally divided window and a corresponding duration, and determining the first identical driving behavior type difference based on a first corresponding relationship among the semantic label, the first driving behavior, the duration corresponding to the first driving behavior, and the behavior type difference;

[0038] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, the first driving behavior in any equally divided window and the corresponding duration are determined, and based on the first corresponding relationship between the semantic label, the first driving behavior, the duration corresponding to the first driving behavior and the behavior type difference, the second same driving behavior type difference is determined.

[0039] Further, the first different driving behavior type difference or the second different driving behavior type difference is determined in the following manner:

[0040] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, selecting at least one equally divided window that does not contain a specified type from all the semantic labels, and determining the first different driving behavior type difference based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and a second corresponding relationship between behavior types;

[0041] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, and at least one equally divided window that does not contain the specified type is selected from all the semantic labels, and based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and the second corresponding relationship between the behavior types, the second different driving behavior type difference is determined.

[0042] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor, wherein the processor is coupled to a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements any of the above methods.

[0043] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above methods.

[0044] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0046] Figure 1 A schematic diagram of a flow chart of a method for identifying a tracked vehicle according to an embodiment of the present invention;

[0047] Figure 2 The present invention is a structural framework diagram of an electronic device. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0049] like Figure 1 As shown, a method for identifying a tracked vehicle according to an embodiment of the present invention comprises the following steps:

[0050] S1, determining a first short-term driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of ​​a target vehicle and determining a second short-term driving behavior semantic sequence of the target vehicle, wherein the first short-term driving behavior semantic sequence represents driving behavior characteristics of the suspected tracking vehicle within a preset period, and the second short-term driving behavior semantic sequence represents driving behavior characteristics of the target vehicle within a preset period;

[0051] S2, calculating the similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence, and when the similarity exceeds a threshold, determining the suspected tracking vehicle as a tracking vehicle.

[0052] The beneficial effects of the vehicle tracking identification method provided by the present invention are as follows:

[0053] The tracking vehicle is identified by calculating the similarity between the short-term driving behavior semantic sequence of vehicles around the owner (suspected tracking vehicle) and the short-term driving behavior semantic sequence of the owner (target vehicle), greatly improving the accuracy and timeliness of tracking vehicle identification.

[0054] S1, determining a first short-term driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of ​​a target vehicle and determining a second short-term driving behavior semantic sequence of the target vehicle, wherein the first short-term driving behavior semantic sequence represents the driving behavior characteristics of the suspected tracking vehicle within a preset period, and the second short-term driving behavior semantic sequence represents the driving behavior characteristics of the target vehicle within a preset period. Wherein:

[0055] The target vehicle refers to any motor vehicle for which a determination is to be made as to whether there is a tracking vehicle.

[0056] The designated surrounding area refers to any area within a range of a preset distance with the target vehicle as the center. It should be noted that there is not only one designated surrounding area, and the amount of data calculation can be determined according to the range of the processing capacity allowed by the processing chip, thereby determining the number of designated surrounding areas.

[0057] A suspected tracking vehicle refers to a motor vehicle or non-motor vehicle that appears in a designated surrounding area for a period exceeding a preset period.

[0058] The first short-term driving behavior semantic sequence refers to the driving behavior of the suspected tracked vehicle for a period of time. Driving behavior refers to driving behavior characteristics, including but not limited to: speed, acceleration, etc.

[0059] The process of determining the first short-term driving behavior semantic sequence may include the following methods:

[0060] Based on the video data collected by the traffic camera, the driving behavior semantic sequence of the suspected tracking vehicle in the collection area of ​​the traffic camera is determined, and multiple window sequences of equal length are obtained after time frame processing. Each window is the first short-term driving behavior semantic sequence. It should be noted that the driving behavior recognition is performed on each window sequence to obtain the semantic label corresponding to each window.

[0061] For example: TD(j) = {SP1, SP2, ..., SP y}

[0062] Among them, TD(j) represents the first short-term driving behavior semantic sequence, SP i is the driving behavior semantic label of the i-th window (each window can correspond to multiple driving behavior semantic labels), i = 1...y, y is the number of windows for frame processing, j is used to identify the serial number of the vehicles around the owner, SP i ={SC i , S.D. i ,ST i , S.U. i , S.E. i};SC i Indicates the type of driving behavior (for example, forward, backward, etc.), SD i Indicates the driving semantic label with the highest distribution ratio in the driving behavior type. Driving behavior semantic labels are classified according to driving behavior types, for example, they can be set as straight, turning, accelerating, decelerating, stopping, turning around, etc. i Indicates the duration segment with the highest duration distribution ratio in the activity type of the driving behavior, SU i Indicates the duration of the highest distribution ratio in this driving behavior type, SE i Indicates the number of driving behavior segments with the highest distribution ratio.

[0063] The driving semantic label with the highest distribution ratio in the driving behavior type may be determined in the following manner:

[0064] In the video data corresponding to the i-th window, determine the driving behavior type corresponding to the i-th window, such as moving forward, determine all driving semantic labels corresponding to the i-th window, such as straight and accelerating, and determine the duration corresponding to each driving semantic label, such as 3s for straight and 1s for acceleration. At this time, the driving semantic label with the highest distribution ratio in the driving behavior type is straight.

[0065] The duration period with the highest distribution ratio in the driving behavior type refers to: the duration period with the highest distribution ratio in the driving behavior type is straight driving. Assuming that the straight driving is continuous for 1-3s and the acceleration is the 4th second, then the duration period with the highest distribution ratio in the driving behavior type is 1-3s.

[0066] The duration segment with the highest duration distribution ratio in the activity type of driving behavior is: 3s.

[0067] The driving behavior segments with the highest distribution ratio are: 1.

[0068] The process of determining the second short-term driving behavior semantic sequence is the same as the process of determining the first short-term driving behavior semantic sequence.

[0069] S2, calculating the similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence, and when the similarity exceeds a threshold, determining the suspected tracking vehicle as a tracking vehicle.

[0070] The process of calculating the similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence is specifically as follows:

[0071] S21, calculate the same driving behavior type difference of the short-term driving behavior semantic sequence, and convert TD i and TD k Determined as any two first short-term driving behavior semantic sequences, the calculation method of the same driving behavior type difference of the second short-term driving behavior semantic sequence is the same as the same driving behavior type difference of the first short-term driving behavior semantic sequence, and the following is explained by taking the same driving behavior type difference of the first short-term driving behavior semantic sequence as an example:

[0072] S211, calculating the difference of a single identical driving behavior type, which is calculated according to the ratio of the time length difference corresponding to the (driving behavior semantic label) to the entire window length, expressed as:

[0073]

[0074] TD i and TD k are the i-th short-term driving behavior semantic sequence and the k-th short-term driving behavior semantic sequence, SC i,j represents the type label of the jth driving behavior in the i-th short-term driving behavior semantic sequence (i.e., the driving behavior type label, the behavior semantic sequence contains multiple driving behavior types), SC k,l represents the type label of the lth driving behavior in the kth short-term driving behavior semantic sequence, |TD i | indicates TD i The length of the time window, Lthsim (SC i,j =s) indicates TD i or TD k The driving behavior type of s is the driving behavior type difference of s (the difference between different driving behavior types can be expressed in many ways, such as Euclidean distance), ST i,j Indicates the duration segment with the highest duration distribution ratio in the jth driving behavior in the i-th short-term driving behavior type semantic sequence, ST k,l represents the duration segment with the highest duration distribution ratio in the lth driving behavior in the kth short-term driving behavior type semantic sequence; it should be noted that when calculating TD i When the corresponding first same driving behavior type is different, the denominator of the above formula is TD i , when calculating TD k When the corresponding second driving behavior type is different, the denominator of the above formula is TD k . The formula is the same for both.

[0075] S212: If there are multiple identical driving behavior types in the short-term driving behavior semantic sequence, then the difference between each driving behavior type is added. The difference between the identical driving behavior types in the short-term driving behavior semantic sequence is expressed as:

[0076]

[0077] Where Dtype sim (TD i ,TD k ) is the short-term driving behavior semantic sequence TD i and TD k The same driving behavior type difference, n is TD i and TD k The number of the same driving behavior types s in .

[0078] Step S22: Calculate the difference between different driving behavior types of the short-term driving behavior semantic sequence. Specifically, step S22 includes:

[0079] S221, calculating the difference between different types of individual driving behaviors, which is calculated according to the ratio of the time length difference corresponding to the (driving behavior semantic label) to the entire window length, expressed as:

[0080]

[0081] Lt dif (SC i,j =s) indicates TD i Contains but TD kThe ratio of the duration of driving behaviors without driving behavior type s to the total duration of the window;

[0082] S222, adding up the individual driving behavior type differences to obtain the driving behavior type difference values. The driving behavior type differences of the short-term driving behavior semantic sequence are expressed as:

[0083]

[0084] Where Dtype dif (TD i ,TD k ) is the short-term driving behavior semantic sequence TD i and TD k The difference between different driving behavior types, n is TD i and TD k There are different driving behavior types s in .

[0085] Step S23: Calculate the driving behavior type difference of the short-term driving behavior semantic sequence. The driving behavior type difference of the short-term driving behavior semantic sequence is obtained by averaging the same driving behavior type difference of the short-term driving behavior semantic sequence and the different driving behavior type differences of the short-term driving behavior semantic sequence, and is expressed as:

[0086]

[0087] Among them, DIS type (TD i ,TD k )∈[0,1], the smaller the value, the more driving behavior type semantic sequence TD i and TD k The higher the similarity, the higher the i and TD k The two vehicles are sequences, and the two vehicles determine the type semantic sequence in the same way, which will not be described in detail here. The result obtained by the above similarity calculation can characterize the similarity of the driving direction and synchronization between the two vehicles. The higher the similarity, the higher the probability that the suspected tracking vehicle is determined to be the tracking vehicle.

[0088] Further, the similarity determination process is:

[0089] Determining a first driving behavior type difference of a corresponding short-term driving behavior semantic sequence according to the first short-term driving behavior semantic sequence, wherein the first driving behavior type difference represents a proportion of the duration of different driving behavior types in the first short-term driving behavior semantic sequence;

[0090] Determine a second driving behavior type difference of a corresponding short-term driving behavior semantic sequence according to the second short-term driving behavior semantic sequence, wherein the second driving behavior type difference represents a time proportion of different driving behavior types in the second short-term driving behavior semantic sequence;

[0091] The similarity is determined based on the first driving behavior type difference and the second driving behavior type difference.

[0092] Further, the first driving behavior type difference / the second driving behavior type difference is determined in the following manner:

[0093] Determine a first same driving behavior type difference and a first different driving behavior type difference of a first short-term driving behavior semantic sequence, and average the first same driving behavior type difference and the first different driving behavior type difference to obtain the first driving behavior type difference;

[0094] Alternatively, a second identical driving behavior type difference and a second different driving behavior type difference of the second short-term driving behavior semantic sequence are determined, and the second identical driving behavior type difference and the second different driving behavior type difference are averaged to obtain the second driving behavior type difference.

[0095] Further, the first same driving behavior type difference or the second same driving behavior type difference is determined in the following manner:

[0096] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, determining a first driving behavior in any equally divided window and a corresponding duration, and determining the first identical driving behavior type difference based on a first corresponding relationship among the semantic label, the first driving behavior, the duration corresponding to the first driving behavior, and the behavior type difference;

[0097] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, the first driving behavior in any equally divided window and the corresponding duration are determined, and based on the first corresponding relationship between the semantic label, the first driving behavior, the duration corresponding to the first driving behavior and the behavior type difference, the second same driving behavior type difference is determined.

[0098] Further, the first different driving behavior type difference or the second different driving behavior type difference is determined in the following manner:

[0099] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, selecting at least one equally divided window that does not contain a specified type from all the semantic labels, and determining the first different driving behavior type difference based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and a second corresponding relationship between behavior types;

[0100] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, and at least one equally divided window that does not contain the specified type is selected from all the semantic labels, and based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and the second corresponding relationship between the behavior types, the second different driving behavior type difference is determined.

[0101] The specified type is a specific type selected by the user.

[0102] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0103] The present invention also provides a tracking vehicle identification system, and the specific technical solution is as follows:

[0104] The determination module is used to: determine a first short-term driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of ​​the target vehicle and determine a second short-term driving behavior semantic sequence of the target vehicle, wherein the first short-term driving behavior semantic sequence represents the driving behavior characteristics of the suspected tracking vehicle within a preset period, and the second short-term driving behavior semantic sequence represents the driving behavior characteristics of the target vehicle within a preset period;

[0105] The calculation module is used to calculate the similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence, and when the similarity exceeds a threshold, determine the suspected tracking vehicle as a tracking vehicle.

[0106] Based on the above solution, the present invention can also be improved as follows.

[0107] Furthermore, the similarity is determined as follows:

[0108] Determine, according to the first short-term driving behavior semantic sequence, a first driving behavior type difference of a corresponding short-term driving behavior semantic sequence, wherein the first driving behavior type difference represents a proportion of the duration of different driving behavior types in the first short-term driving behavior semantic sequence;

[0109] Determine a second driving behavior type difference of a corresponding short-term driving behavior semantic sequence according to the second short-term driving behavior semantic sequence, wherein the second driving behavior type difference represents a time proportion of different driving behavior types in the second short-term driving behavior semantic sequence;

[0110] The similarity is determined based on the first driving behavior type difference and the second driving behavior type difference.

[0111] Further, the first driving behavior type difference / the second driving behavior type difference is determined in the following manner:

[0112] Determine a first same driving behavior type difference and a first different driving behavior type difference of a first short-term driving behavior semantic sequence, and average the first same driving behavior type difference and the first different driving behavior type difference to obtain the first driving behavior type difference;

[0113] Alternatively, a second identical driving behavior type difference and a second different driving behavior type difference of the second short-term driving behavior semantic sequence are determined, and the second identical driving behavior type difference and the second different driving behavior type difference are averaged to obtain the second driving behavior type difference.

[0114] Further, the first same driving behavior type difference or the second same driving behavior type difference is determined in the following manner:

[0115] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, determining a first driving behavior in any equally divided window and a corresponding duration, and determining the first identical driving behavior type difference based on a first corresponding relationship among the semantic label, the first driving behavior, the duration corresponding to the first driving behavior, and the behavior type difference;

[0116] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, the first driving behavior in any equally divided window and the corresponding duration are determined, and based on the first corresponding relationship between the semantic label, the first driving behavior, the duration corresponding to the first driving behavior and the behavior type difference, the second same driving behavior type difference is determined.

[0117] Further, the first different driving behavior type difference or the second different driving behavior type difference is determined in the following manner:

[0118] Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, selecting at least one equally divided window that does not contain a specified type from all the semantic labels, and determining the first different driving behavior type difference based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and a second corresponding relationship between behavior types;

[0119] Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, and at least one equally divided window that does not contain the specified type is selected from all the semantic labels, and based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and the second corresponding relationship between the behavior types, the second different driving behavior type difference is determined.

[0120] It should be noted that the beneficial effects of the vehicle tracking identification system provided by the above embodiment are the same as the beneficial effects of the vehicle tracking identification method provided by the above embodiment, which will not be described in detail here. In addition, when the system provided by the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided by the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described in detail here.

[0121] like Figure 2 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above methods, specifically:

[0122] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements a tracking vehicle identification method provided by the above embodiment. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for realizing device functions, which will not be repeated here.

[0123] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any one of the above methods.

[0124] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0125] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs any of the above methods.

[0126] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0127] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0128] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0129] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for identifying a tracked vehicle, characterized in that: include: Determine a first short-term driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of ​​the target vehicle and determine a second short-term driving behavior semantic sequence of the target vehicle, wherein the first short-term driving behavior semantic sequence represents driving behavior characteristics of the suspected tracking vehicle within a preset period, and the second short-term driving behavior semantic sequence represents driving behavior characteristics of the target vehicle within a preset period; The similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence is calculated, and when the similarity exceeds a threshold, the suspected tracking vehicle is determined as a tracking vehicle.

2. A method for identifying a tracked vehicle according to claim 1, characterized in that: The similarity determination process is: Determine, according to the first short-term driving behavior semantic sequence, a first driving behavior type difference of a corresponding short-term driving behavior semantic sequence, wherein the first driving behavior type difference represents a proportion of the duration of different driving behavior types in the first short-term driving behavior semantic sequence; Determine a second driving behavior type difference of a corresponding short-term driving behavior semantic sequence according to the second short-term driving behavior semantic sequence, wherein the second driving behavior type difference represents a time proportion of different driving behavior types in the second short-term driving behavior semantic sequence; The similarity is determined based on the first driving behavior type difference and the second driving behavior type difference.

3. A method for identifying a tracked vehicle according to claim 2, characterized in that: The first driving behavior type difference / the second driving behavior type difference is determined in the following manner: Determine a first same driving behavior type difference and a first different driving behavior type difference of a first short-term driving behavior semantic sequence, and average the first same driving behavior type difference and the first different driving behavior type difference to obtain the first driving behavior type difference; Alternatively, a second identical driving behavior type difference and a second different driving behavior type difference of the second short-term driving behavior semantic sequence are determined, and the second identical driving behavior type difference and the second different driving behavior type difference are averaged to obtain the second driving behavior type difference.

4. A method for identifying a tracked vehicle according to claim 3, characterized in that: The first same driving behavior type difference or the second same driving behavior type difference is determined as follows: Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, determining a first driving behavior in any equally divided window and a corresponding duration, and determining the first identical driving behavior type difference based on a first corresponding relationship among the semantic label, the first driving behavior, the duration corresponding to the first driving behavior, and the behavior type difference; Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, the first driving behavior in any equally divided window and the corresponding duration are determined, and based on the first corresponding relationship between the semantic label, the first driving behavior, the duration corresponding to the first driving behavior and the behavior type difference, the second same driving behavior type difference is determined.

5. A method for identifying a tracked vehicle according to claim 3, characterized in that: The first different driving behavior type difference or the second different driving behavior type difference is determined in the following manner: Dividing the first short-term driving behavior semantic sequence into a plurality of equally divided windows, and determining a semantic label corresponding to each equally divided window, selecting at least one equally divided window that does not contain a specified type from all the semantic labels, and determining the first different driving behavior type difference based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and a second corresponding relationship between behavior types; Alternatively, the second short-term driving behavior semantic sequence is divided into multiple equally divided windows, and the semantic label corresponding to each equally divided window is determined, and at least one equally divided window that does not contain the specified type is selected from all the semantic labels, and based on the number of equally divided windows that do not contain the specified type, the total duration corresponding to all equally divided windows that do not contain the specified type, and the second corresponding relationship between the behavior types, the second different driving behavior type difference is determined.

6. A vehicle tracking identification system, characterized in that: include: The determination module is used to: determine a first short-term driving behavior semantic sequence corresponding to a suspected tracking vehicle in a designated surrounding area of ​​the target vehicle and determine a second short-term driving behavior semantic sequence of the target vehicle, wherein the first short-term driving behavior semantic sequence represents the driving behavior characteristics of the suspected tracking vehicle within a preset period, and the second short-term driving behavior semantic sequence represents the driving behavior characteristics of the target vehicle within a preset period; The calculation module is used to calculate the similarity between the first short-term driving behavior semantic sequence and the second short-term driving behavior semantic sequence, and when the similarity exceeds a threshold, determine the suspected tracking vehicle as a tracking vehicle.

7. A vehicle tracking identification system according to claim 6, characterized in that: The similarity determination process is: Determine, according to the first short-term driving behavior semantic sequence, a first driving behavior type difference of a corresponding short-term driving behavior semantic sequence, wherein the first driving behavior type difference represents a proportion of the duration of different driving behavior types in the first short-term driving behavior semantic sequence; Determine a second driving behavior type difference of a corresponding short-term driving behavior semantic sequence according to the second short-term driving behavior semantic sequence, wherein the second driving behavior type difference represents a time proportion of different driving behavior types in the second short-term driving behavior semantic sequence; The similarity is determined based on the first driving behavior type difference and the second driving behavior type difference.

8. A vehicle tracking identification system according to claim 7, characterized in that: The first driving behavior type difference / the second driving behavior type difference is determined in the following manner: Determine a first same driving behavior type difference and a first different driving behavior type difference of a first short-term driving behavior semantic sequence, and average the first same driving behavior type difference and the first different driving behavior type difference to obtain the first driving behavior type difference; Alternatively, a second identical driving behavior type difference and a second different driving behavior type difference of the second short-term driving behavior semantic sequence are determined, and the second identical driving behavior type difference and the second different driving behavior type difference are averaged to obtain the second driving behavior type difference.

9. An electronic device, characterized in that: The electronic device comprises a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements the method according to any one of claims 1 to 5.

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