CN114416893A - Method and system for processing vehicle monitoring video - Google Patents

Abstract

According to the vehicle monitoring video processing method and system, the vehicle migration vector set is obtained by combining with the primary screening of the monitoring equipment, and then the vehicle migration vector set is monitored and processed through the artificial intelligence thread. Therefore, the vehicle travel track data can be used for primarily screening the vehicle migration vector set through the monitoring equipment, so that the interference of the non-vehicle migration vector set on the artificial intelligence thread is avoided as much as possible, and the accuracy and the reliability of vehicle age monitoring are improved.

Description

Method and system for processing vehicle monitoring video

Technical Field

The application relates to the technical field of data matching, in particular to a method and a system for processing a vehicle monitoring video.

Background

In the actual operation process, the inventor finds that the monitoring is not in place in the process of monitoring the running of the vehicle and the like. Therefore, a technical solution is needed to improve the processing of the vehicle surveillance video.

Disclosure of Invention

In view of this, the present application provides a method and a system for processing a vehicle surveillance video.

In a first aspect, a method for processing a vehicle surveillance video is provided, which is applied to a system for processing a vehicle surveillance video, and the method at least includes:

obtaining vehicle running track data to be processed;

obtaining a vehicle migration vector set mined by the monitoring equipment in the vehicle running track data;

determining a first vehicle running vector of the vehicle migration vector set by the monitoring equipment, and mining a vehicle running theme of the vehicle migration vector set in the vehicle running track data according to an artificial intelligence thread;

determining a first vehicle movement scenario for the set of vehicle migration vectors that originates from the artificial intelligence thread.

In a separately implemented embodiment, the method further comprises:

determining the first vehicle motion condition as the vehicle motion condition of the vehicle running track data, and uploading the vehicle motion condition of the vehicle running track data;

or obtaining a second vehicle running vector of the monitoring equipment to the first vehicle motion condition, and excavating a vehicle running theme related to the first vehicle motion condition according to the artificial intelligence thread;

determining a second vehicle movement condition associated with the first vehicle movement condition that originates from the artificial intelligence thread;

and determining the second vehicle motion situation as the vehicle motion situation of the vehicle running track data, and uploading the vehicle motion situation of the vehicle running track data.

In a separately implemented embodiment, the method further comprises:

obtaining a vehicle motion condition of vehicle running track data to be processed, wherein the vehicle motion condition of the vehicle running track data to be processed represents a key event description set in the vehicle running track data to be processed;

determining a third vehicle running vector of the monitoring equipment to the vehicle running track data to be processed, and excavating a vehicle running theme of a key event description set in the vehicle running track data to be processed according to an artificial intelligence thread;

determining a third vehicle movement scenario for the set of key event descriptions that originates from the artificial intelligence thread;

and determining the third vehicle motion condition as the detection condition of the vehicle running track data, and uploading the detection condition of the vehicle running track data.

In an independently implemented embodiment, the obtaining a set of vehicle migration vectors mined by the monitoring device in the vehicle driving trajectory data includes: obtaining a vehicle migration vector set mined in the vehicle driving track data by a first monitoring device thread;

the determining of the third vehicle driving vector of the monitoring device on the vehicle driving track data to be processed includes the following steps of mining a vehicle driving theme of a key event description set in the vehicle driving track data to be processed according to an artificial intelligence thread, and the method includes: and determining a third vehicle running vector of the second monitoring equipment thread to the vehicle running track data to be processed, and excavating a vehicle running theme of a key event description set in the vehicle running track data to be processed according to the artificial intelligence thread.

In an independently implemented embodiment, after obtaining the vehicle motion profile of the vehicle driving trajectory data to be processed, the method further comprises: identifying a key event description set in the vehicle driving track data to be processed by combining an intelligent monitoring thread in second monitoring equipment;

before the determining the third vehicle motion situation as the detection situation of the vehicle driving track data and uploading the detection situation of the vehicle driving track data, the method further comprises: and identifying the motion condition of the third vehicle by combining an intelligent monitoring thread in the second monitoring device.

In an independently implemented embodiment, on the premise that the step of determining the vehicle driving subject of the monitoring device for the first vehicle driving vector of the vehicle migration vector set and mining the key event description set in the vehicle driving track data to be processed according to the artificial intelligence thread, the method further includes:

and determining the vehicle migration vector set as the vehicle motion condition of the vehicle running track data, and uploading the vehicle motion condition of the vehicle running track data.

In an independently implemented embodiment, the obtaining a set of vehicle migration vectors mined by the monitoring device in the vehicle driving trajectory data includes:

and obtaining a vehicle migration vector set mined by the monitoring equipment in the locally repeated driving behavior events in the vehicle driving track data, wherein the vehicle migration vector set covers the global target driving behavior events and does not cover the global non-target driving behavior events.

In a second aspect, a vehicle surveillance video processing system is provided, which includes a processor and a memory, which are communicated with each other, and the processor is configured to read a computer program from the memory and execute the computer program, so as to implement the method.

According to the method and the system for processing the vehicle monitoring video, which are provided by the embodiment of the application, the vehicle migration vector set is obtained by combining with the primary screening of the monitoring equipment, and then the vehicle migration vector set is monitored and processed through the artificial intelligence thread. Therefore, the vehicle travel track data can be used for primarily screening the vehicle migration vector set through the monitoring equipment, so that the interference of the non-vehicle migration vector set on the artificial intelligence thread is avoided as much as possible, and the accuracy and the reliability of vehicle age monitoring are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart of a method for processing a vehicle surveillance video according to an embodiment of the present disclosure.

Fig. 2 is a block diagram of a processing device for vehicle monitoring video according to an embodiment of the present disclosure.

Fig. 3 is an architecture diagram of a processing system for vehicle surveillance video according to an embodiment of the present application.

Detailed Description

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

Referring to fig. 1, a method for processing a vehicle surveillance video is shown, which may include the technical solutions described in the following steps 100-400.

And step 100, obtaining vehicle running track data to be processed.

And 200, obtaining a vehicle migration vector set mined by the monitoring equipment in the vehicle running track data.

Step 300, determining a first vehicle running vector of the vehicle migration vector set by the monitoring device, and mining a vehicle running theme of the vehicle migration vector set in the vehicle running track data according to the artificial intelligence thread.

Step 400, determining a first vehicle motion situation of the vehicle migration vector set, which is originated from the artificial intelligence thread.

It is understood that, when the contents described in the above steps 100 to 400 are executed, a vehicle migration vector set is obtained in conjunction with the initial screening of the monitoring device, and then the vehicle migration vector set is subjected to monitoring processing through an artificial intelligence thread. Therefore, the vehicle travel track data can be used for primarily screening the vehicle migration vector set through the monitoring equipment, so that the interference of the non-vehicle migration vector set on the artificial intelligence thread is avoided as much as possible, and the accuracy and the reliability of vehicle age monitoring are improved.

Based on the above basis, the following descriptions of step s 11-step s14 can also be included.

And step s11, determining the first vehicle motion situation as the vehicle motion situation of the vehicle running track data, and uploading the vehicle motion situation of the vehicle running track data.

Step s12, or obtaining a second vehicle running vector of the monitoring device to the first vehicle motion situation, and excavating a vehicle running theme related to the first vehicle motion situation according to the artificial intelligence thread.

Step s13 determines a second vehicle movement profile associated with the first vehicle movement profile originating from the artificial intelligence thread.

And step s14, determining the second vehicle motion situation as the vehicle motion situation of the vehicle running track data, and uploading the vehicle motion situation of the vehicle running track data.

It can be understood that, when the contents described in the above-described steps s11 to s14 are executed, the reliability of the vehicle motion situation of uploading the vehicle travel track data can be improved by accurately determining the vehicle travel track data.

Based on the above basis, the following descriptions of step s 21-step s24 can also be included.

And step s21, obtaining the vehicle motion condition of the vehicle driving track data to be processed, wherein the vehicle motion condition of the vehicle driving track data to be processed represents the key event description set in the vehicle driving track data to be processed.

Step s22 is determining a third vehicle driving vector of the vehicle driving track data to be processed by the monitoring device, and excavating the vehicle driving subject of the key event description set in the vehicle driving track data to be processed according to the artificial intelligence thread.

At step s23, a third vehicle movement scenario for the set of key event descriptions originating from the artificial intelligence thread is determined.

And step s24, determining the third vehicle motion situation as the detection situation of the vehicle running track data, and uploading the detection situation of the vehicle running track data.

It can be understood that, when the contents described in the above-mentioned steps s21 to s24 are executed, the accuracy of the detection situation of the uploaded vehicle travel track data can be improved by continuously mining the vehicle travel track data to be processed.

In the embodiment of the present disclosure, when obtaining the vehicle migration vector set mined by the monitoring device in the vehicle driving track data, there is a problem of mining error, so that it is difficult to accurately mine the vehicle migration vector set, and in order to improve the above technical problem, the step of obtaining the vehicle migration vector set mined by the monitoring device in the vehicle driving track data described in step 200 may specifically include the content described in step 210 below.

And step 210, obtaining a vehicle migration vector set mined in the vehicle running track data by the first monitoring device thread.

It can be understood that, when the content described in the above step 210 is executed, the problem of mining errors is improved when the vehicle migration vector set mined in the vehicle driving track data by the monitoring device is obtained, so that the vehicle migration vector set can be accurately mined.

In the embodiment of the present disclosure, when determining the third vehicle driving vector of the vehicle driving trajectory data to be processed by the monitoring device, there is a problem that identification is inaccurate, so that it is difficult to accurately obtain the third vehicle driving vector, and in order to improve the above technical problem, the step of determining the third vehicle driving vector of the vehicle driving trajectory data to be processed by the monitoring device, which is described in step s23, mining the vehicle driving theme of the key event description set in the vehicle driving trajectory data to be processed according to an artificial intelligence thread may specifically include what is described in the following step s 231.

Step s231, determining a third vehicle driving vector of the second monitoring device thread to the vehicle driving track data to be processed, and excavating a vehicle driving theme of a key event description set in the vehicle driving track data to be processed according to the artificial intelligence thread.

It can be understood that, when the third vehicle travel vector of the vehicle travel track data to be processed by the monitoring device is determined while the content described in the above step s231 is executed, the problem of inaccurate identification is improved, so that the third vehicle travel vector can be accurately obtained.

Based on the above basis, after obtaining the vehicle motion situation of the vehicle driving trace data to be processed, the following contents described in step s31 may be further included.

And step s31, identifying a key event description set in the vehicle driving track data to be processed by combining with an intelligent monitoring thread in the second monitoring device.

It can be understood that when the content described in the above step s31 is executed, the set of key event descriptions can be identified to the greatest extent, so as to reduce the workload of the subsequent work.

Based on the above, before the determining the third vehicle motion situation as the detection situation of the vehicle driving trace data and uploading the detection situation of the vehicle driving trace data, the following contents described in step s51 may be further included.

And step s51, combining the intelligent monitoring thread in the second monitoring device to identify the motion situation of the third vehicle.

It can be understood that when the content described in the above step s51 is executed, the accuracy of identification is improved by multi-dimensionally identifying, so that the workload of subsequent work can be effectively reduced.

Based on the above basis, on the premise that the step of mining the vehicle driving theme of the key event description set in the to-be-processed vehicle driving trajectory data according to the artificial intelligence thread for the first vehicle driving vector of the vehicle migration vector set, the determining and monitoring device may further include the content described in the following step a 1.

Step a1, determining the vehicle migration vector set as the vehicle motion condition of the vehicle running track data, and uploading the vehicle motion condition of the vehicle running track data.

It can be understood that, when the content described in the step a1 is executed, the vehicle migration vector set is analyzed, so as to improve the accuracy of the vehicle motion situation of uploading the vehicle driving track data.

In this embodiment, when obtaining the vehicle migration vector set mined by the monitoring device in the vehicle driving trace data, there is a case where the vehicle driving trace data is repeated, thereby causing a problem of disordered mining of the driving behavior event, and in order to improve the above technical problem, the step of obtaining the vehicle migration vector set mined by the monitoring device in the vehicle driving trace data described in step 200 may specifically include the content described in step d 1.

And d1, obtaining a vehicle migration vector set mined by the monitoring equipment in the locally repeated driving behavior events in the vehicle driving track data, wherein the vehicle migration vector set covers the global target driving behavior events and does not cover the global non-target driving behavior events.

It can be understood that when the content described in the above step d1 is executed, the condition that the vehicle travel track data are duplicated is improved when the vehicle migration vector set mined in the vehicle travel track data by the monitoring device is obtained, so as to avoid the problem of mining disorder of the travel behavior event.

On the basis, please refer to fig. 2 in combination, there is provided a processing apparatus 200 for vehicle surveillance video, which is applied to a processing system for vehicle surveillance video, the apparatus includes:

a data obtaining module 210, configured to obtain vehicle driving track data to be processed;

a vector obtaining module 220, configured to obtain a vehicle migration vector set mined by the monitoring device in the vehicle driving trajectory data;

the theme mining module 230 is configured to determine a first vehicle driving vector of the vehicle migration vector set by the monitoring device, and mine a vehicle driving theme of the vehicle migration vector set in the vehicle driving track data according to an artificial intelligence thread;

a situation determination module 240 for determining a first vehicle movement situation for the set of vehicle migration vectors that originates from the artificial intelligence thread.

On the basis of the above, please refer to fig. 3, which shows a processing system 300 for vehicle monitoring video, comprising a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.

On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.

In summary, based on the above scheme, a vehicle migration vector set is obtained by combining with the primary screening of the monitoring device, and then the vehicle migration vector set is monitored and processed through an artificial intelligence thread. Therefore, the vehicle travel track data can be used for primarily screening the vehicle migration vector set through the monitoring equipment, so that the interference of the non-vehicle migration vector set on the artificial intelligence thread is avoided as much as possible, and the accuracy and the reliability of vehicle age monitoring are improved.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code 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. In the latter scenario, the remote computer may be connected to the user's computer through any suitable means, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service using, for example, software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (8)

1. A processing method of vehicle monitoring video is characterized by being applied to a processing system of vehicle monitoring video, and the method at least comprises the following steps:

obtaining vehicle running track data to be processed;

obtaining a vehicle migration vector set mined by the monitoring equipment in the vehicle running track data;

determining a first vehicle running vector of the vehicle migration vector set by the monitoring equipment, and mining a vehicle running theme of the vehicle migration vector set in the vehicle running track data according to an artificial intelligence thread;

determining a first vehicle movement scenario for the set of vehicle migration vectors that originates from the artificial intelligence thread.

2. The method for processing vehicle surveillance video according to claim 1, further comprising:

determining the first vehicle motion condition as the vehicle motion condition of the vehicle running track data, and uploading the vehicle motion condition of the vehicle running track data;

or obtaining a second vehicle running vector of the monitoring equipment to the first vehicle motion condition, and excavating a vehicle running theme related to the first vehicle motion condition according to the artificial intelligence thread;

determining a second vehicle movement condition associated with the first vehicle movement condition that originates from the artificial intelligence thread;

and determining the second vehicle motion situation as the vehicle motion situation of the vehicle running track data, and uploading the vehicle motion situation of the vehicle running track data.

3. The method for processing vehicle surveillance video according to claim 1, further comprising:

obtaining a vehicle motion condition of vehicle running track data to be processed, wherein the vehicle motion condition of the vehicle running track data to be processed represents a key event description set in the vehicle running track data to be processed;

determining a third vehicle running vector of the monitoring equipment to the vehicle running track data to be processed, and excavating a vehicle running theme of a key event description set in the vehicle running track data to be processed according to an artificial intelligence thread;

determining a third vehicle movement scenario for the set of key event descriptions that originates from the artificial intelligence thread;

and determining the third vehicle motion condition as the detection condition of the vehicle running track data, and uploading the detection condition of the vehicle running track data.

4. The method for processing the vehicle monitoring video according to claim 3, wherein the obtaining a set of vehicle migration vectors mined by the monitoring device in the vehicle driving track data comprises: obtaining a vehicle migration vector set mined in the vehicle driving track data by a first monitoring device thread;

the determining of the third vehicle driving vector of the monitoring device on the vehicle driving track data to be processed includes the following steps of mining a vehicle driving theme of a key event description set in the vehicle driving track data to be processed according to an artificial intelligence thread, and the method includes: and determining a third vehicle running vector of the second monitoring equipment thread to the vehicle running track data to be processed, and excavating a vehicle running theme of a key event description set in the vehicle running track data to be processed according to the artificial intelligence thread.

5. The method for processing the vehicle surveillance video according to claim 3, wherein after obtaining the vehicle motion condition of the vehicle travel track data to be processed, the method further comprises: identifying a key event description set in the vehicle driving track data to be processed by combining an intelligent monitoring thread in second monitoring equipment;

before the determining the third vehicle motion situation as the detection situation of the vehicle driving track data and uploading the detection situation of the vehicle driving track data, the method further comprises: and identifying the motion condition of the third vehicle by combining an intelligent monitoring thread in the second monitoring device.

6. The method for processing vehicle surveillance video according to claim 1, wherein said determining a first vehicle driving vector of said set of vehicle migration vectors by the surveillance device is based on a step of mining a vehicle driving subject of said set of key event descriptions in the vehicle driving trace data to be processed according to an artificial intelligence thread, and wherein said method further comprises:

and determining the vehicle migration vector set as the vehicle motion condition of the vehicle running track data, and uploading the vehicle motion condition of the vehicle running track data.

7. The method for processing the vehicle monitoring video according to claim 1, wherein the obtaining a set of vehicle migration vectors mined by the monitoring device in the vehicle driving track data comprises:

and obtaining a vehicle migration vector set mined by the monitoring equipment in the locally repeated driving behavior events in the vehicle driving track data, wherein the vehicle migration vector set covers the global target driving behavior events and does not cover the global non-target driving behavior events.

8. A processing system for vehicle surveillance video, comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of any one of claims 1 to 7.

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