CN112700653A

CN112700653A - Method, device and equipment for judging illegal lane change of vehicle and storage medium

Info

Publication number: CN112700653A
Application number: CN202011520289.1A
Authority: CN
Inventors: 胡威
Original assignee: Shanghai Eye Control Technology Co Ltd
Current assignee: Shanghai Eye Control Technology Co Ltd
Priority date: 2020-12-21
Filing date: 2020-12-21
Publication date: 2021-04-23

Abstract

The embodiment of the invention discloses a method for judging illegal lane change of a vehicle, which comprises the following steps: preprocessing an original video to obtain a video to be identified; determining a running track of a target vehicle in a video to be identified, and obtaining a target video corresponding to continuous running of the target vehicle; segmenting a target video to obtain at least one video segment; classifying at least one video clip to obtain a driving category corresponding to each video clip; and if the set type exists in the driving types, the target vehicle has illegal lane changing behaviors. According to the method for judging the illegal lane change of the vehicle, which is disclosed by the embodiment of the invention, the target identification and the video classification are carried out on the video, so that the automatic judgment on whether the illegal lane change of the vehicle exists in the video can be realized, the labor cost is saved, and the working efficiency is improved.

Description

Method, device and equipment for judging illegal lane change of vehicle and storage medium

Technical Field

The invention relates to the technical field of image processing, in particular to a method, a device, equipment and a storage medium for judging illegal lane change of a vehicle.

Background

With the continuous change of the deep learning technology, the field of intelligent transportation is always an important hot branch of the deep learning technology landing. The image classification, target detection, image segmentation and other technologies based on the convolutional neural network are widely applied to aspects of scene recognition, target behavior analysis and the like, provide traffic auxiliary information for pedestrians, drivers and the like, and aim to enable traffic travel to be more intelligent and convenient.

Disclosure of Invention

The embodiment of the invention provides a method, a device, equipment and a storage medium for judging illegal lane changing of a vehicle, which can realize automatic judgment on whether the vehicle has illegal lane changing behaviors in a video, thereby saving labor cost and improving working efficiency.

In a first aspect, an embodiment of the present invention provides a method for determining a lane change violation of a vehicle, including:

preprocessing an original video to obtain a video to be identified;

determining a running track of a target vehicle in the video to be identified, and obtaining a target video corresponding to continuous running of the target vehicle;

segmenting the target video to obtain at least one video segment;

classifying the at least one video clip to obtain a driving category corresponding to each video clip;

and if the set type exists in the driving types, the target vehicle has illegal lane changing behaviors.

Further, preprocessing the original video to obtain a video to be identified, including:

segmenting an original video to obtain a plurality of sub-videos;

screening the plurality of sub-videos to obtain sub-videos meeting conditions;

and splicing the screened sub-videos to obtain the video to be identified.

Further, determining a running track of a target vehicle in the video to be identified, and obtaining a target video corresponding to continuous running of the target vehicle, including:

carrying out target tracking on the video to be identified to obtain an initial driving track;

and correcting the initial running track to obtain the final running track of the target vehicle.

Further, correcting the initial travel track includes:

performing target identification on a video frame in the video to be identified to obtain first characteristic information of the target vehicle;

acquiring second characteristic information of the target vehicle in each video frame corresponding to the initial driving track;

and reserving the video frame of which the second characteristic information is matched with the first characteristic information.

Further, the driving track is formed by a detection frame of a target vehicle in each video frame, and a target video corresponding to continuous driving of the target vehicle is obtained, including:

adjusting the detection frame in each video frame according to a set proportion;

and splicing the adjusted video frames to obtain a target video.

Further, segmenting the target video to obtain at least one video segment, including:

and segmenting the target video according to the set frame number to obtain at least one video segment.

Further, classifying the at least one video clip to obtain a driving category corresponding to each video clip includes:

inputting the at least one video clip into a set classification model to obtain a driving category corresponding to each video clip; wherein the travel category includes: pressing dotted lines, pressing lines and not changing;

correspondingly, if the set type exists in the driving types, the illegal lane change behavior of the target vehicle includes:

and if the corresponding driving category of each video clip has the compaction line lane change, the target vehicle has illegal lane change behaviors.

In a second aspect, an embodiment of the present invention further provides a device for determining a lane change due to vehicle violation, including:

the to-be-identified video acquisition module is used for preprocessing the original video to acquire the to-be-identified video;

the target video obtaining module is used for determining the running track of a target vehicle in the video to be identified and obtaining a target video corresponding to the continuous running of the target vehicle;

the video clip acquisition module is used for segmenting the target video to acquire at least one video clip;

the driving category obtaining module is used for classifying the at least one video clip to obtain the driving categories corresponding to the video clips;

and the illegal behavior determination module is used for determining that the target vehicle has illegal lane changing behaviors if the set type exists in the driving types.

Optionally, the to-be-identified video acquisition module is further configured to:

segmenting an original video to obtain a plurality of sub-videos;

screening the plurality of sub-videos to obtain sub-videos meeting conditions;

and splicing the screened sub-videos to obtain the video to be identified.

Optionally, the target video obtaining module is further configured to:

and splicing the adjusted video frames to obtain a target video.

Optionally, the video clip obtaining module is further configured to:

Optionally, the driving category obtaining module is further configured to:

inputting the at least one video clip into a set classification model to obtain a driving category corresponding to each video clip; wherein the travel category includes: and changing the lane by pressing a dotted line, and changing the lane and not changing the lane by pressing a compacted line.

Correspondingly, the illegal behavior determination module is further used for:

In a third aspect, an embodiment of the present invention further provides an apparatus for determining an illegal lane change, where the apparatus includes:

comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for illegal lane change determination according to any of the embodiments of the present invention when executing the program.

In a fourth aspect, the embodiment of the present invention further provides a storage medium for determining illegal lane change, where a computer program is stored, and the computer program, when executed by a processing device, implements the method for determining illegal lane change of a vehicle according to any one of the embodiments of the present invention.

The method comprises the steps of firstly preprocessing an original video to obtain a video to be identified; then determining the running track of the target vehicle in the video to be identified, and obtaining a target video corresponding to the continuous running of the target vehicle; then segmenting the target video to obtain at least one video segment; finally, classifying at least one video clip to obtain a driving category corresponding to each video clip; and if the set type exists in the driving types, the target vehicle has illegal lane changing behaviors. According to the method for judging the illegal lane change of the vehicle, which is disclosed by the embodiment of the invention, the target identification and the video classification are carried out on the video, so that the automatic judgment on whether the illegal lane change of the vehicle exists in the video can be realized, the labor cost is saved, and the working efficiency is improved.

Drawings

FIG. 1 is a flowchart of a method for determining an illegal lane change of a vehicle according to a first embodiment of the present invention;

FIG. 2 is a schematic diagram illustrating a process for determining an illegal lane change of a vehicle according to a first embodiment of the present invention;

FIG. 3 is a flowchart of a method for obtaining a target video according to a second embodiment of the present invention;

FIG. 4 is a schematic structural diagram of a device for determining an illegal lane change of a vehicle according to a third embodiment of the present invention;

fig. 5 is a schematic structural diagram of a computer device in the fourth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Example one

Fig. 1 is a flowchart of a method for determining a vehicle illegal lane change according to an embodiment of the present invention, where the embodiment is applicable to a situation where a vehicle illegal lane change is determined, and the method may be executed by a device for determining a vehicle illegal lane change, where the device may be composed of hardware and/or software, and may generally be integrated into a device having a function of determining a vehicle illegal lane change, where the device may be an electronic device such as a server or a server cluster. As shown in fig. 1, the method specifically comprises the following steps:

and step 110, preprocessing the original video to obtain a video to be identified.

Preprocessing the original video can be understood as a process of filtering the original video. Specifically, each frame in the original video can be identified, and video frames meeting the conditions are screened out; or segmenting the original video into video segments, identifying each video segment, and filtering out the video segments which do not meet the conditions. In this embodiment, in order to improve the preprocessing efficiency, a mode of filtering video clips is adopted.

In this embodiment, the original video is preprocessed, and the manner of obtaining the video to be identified may be: segmenting an original video to obtain a plurality of sub-videos; screening a plurality of sub-videos to obtain sub-videos meeting conditions; and splicing the screened sub-videos to obtain the video to be identified.

Preferably, since the original video may include a plurality of types and the occurrence situation is complicated, the original video may be preprocessed to obtain the video to be recognized. Specifically, the original video may be segmented into a plurality of shorter sub-videos, for example, the original video may be segmented once every 3s to obtain a plurality of sub-videos with a duration not greater than 3s, and then the sub-videos are screened. Preferably, the sub-videos can be input into the classification model during screening to classify scenes appearing in the videos, one is that a target vehicle stably runs on a road, and the other is that irregular videos such as video inversion, large shaking angle, splicing and blurring caused by irregular video shooting can be reserved for the first video, and the second video can be filtered. After screening, the reserved sub-videos can be spliced, so that a complete video to be identified is obtained.

And step 120, determining the running track of the target vehicle in the video to be identified, and obtaining the target video corresponding to the continuous running of the target vehicle.

Wherein the driving track is formed by the position of the vehicle in each video frame.

In this embodiment, the running track of the target vehicle in the video to be recognized is determined, and the manner of obtaining the target video corresponding to the continuous running of the target vehicle may be: carrying out target tracking on a video to be identified to obtain an initial driving track; and correcting the initial running track to obtain the final running track of the target vehicle.

Further, the manner of correcting the initial travel track may be: carrying out target identification on a video frame in a video to be identified to obtain first characteristic information of a target vehicle; acquiring second characteristic information of the target vehicle in each video frame corresponding to the initial driving track; and reserving the video frame of which the second characteristic information is matched with the first characteristic information.

In this embodiment, the driving track is formed by the detection frame of the target vehicle in each video frame, and the manner of obtaining the target video corresponding to the continuous driving of the target vehicle may be: adjusting the detection frame in each video frame according to a set proportion; and splicing the adjusted video frames to obtain a target video.

Step 130, segmenting the target video to obtain at least one video segment.

In this embodiment, the method for segmenting the target video and obtaining at least one video segment may be as follows: and segmenting the target video according to the set frame number to obtain at least one video segment.

Since a video may contain multiple behaviors such as multiple lane changes, it is necessary to cut the target video into small segments for analysis. Preferably, the number of the small segments to be cut can be determined by setting a specific frame number, for example, for a target video of 10s, sequentially cutting back until the whole video is cut off according to the frame number of 3 × fps as a cut-off threshold, where fps is the video frame rate. In doing so, 3 video segments of 3s duration and 1 video segment of 1s duration can be obtained, for a total of 4.

And step 140, classifying at least one video clip to obtain the driving categories corresponding to the video clips.

In this embodiment, the manner of classifying at least one video clip and obtaining the driving category corresponding to each video clip may be as follows: and inputting at least one video clip into a set classification model to obtain the driving categories corresponding to the video clips. Wherein the driving categories include: and changing the lane by pressing a dotted line, and changing the lane and not changing the lane by pressing a compacted line.

Preferably, the video segments obtained by segmentation may be input into a classification model, and in this embodiment, the driving categories corresponding to the video segments may be obtained by model output, including lane change by pressed dotted lines, lane change by pressed lines, and lane change without pressing lines.

And 150, if the set type exists in the driving types, the target vehicle has illegal lane change behaviors.

In this embodiment, the manner of determining that the target vehicle has the illegal lane change behavior may be: and if the compaction line lane change exists in the driving category corresponding to each video clip, the target vehicle has illegal lane change behaviors.

Specifically, if the compaction line lane change exists in the output result of the classification model, it is determined that the target vehicle has illegal lane change behavior.

Fig. 2 is a schematic diagram of a process for determining an illegal lane change of a vehicle in an embodiment of the present invention, and as shown in fig. 2, a video to be identified is obtained by preprocessing an original video, then a target detection frame is obtained by performing target tracking and target detection, and a video is captured and input to a classification model after the detection frame is adjusted to obtain a corresponding driving category.

Example two

Fig. 3 is a flowchart of a method for obtaining a target video according to a second embodiment of the present invention, where this embodiment is applicable to a case of obtaining a target video corresponding to continuous driving of a target vehicle, as shown in fig. 3, the method specifically includes the following steps:

and step 121, carrying out target tracking on the video to be recognized to obtain an initial driving track.

Preferably, a target tracking algorithm SiamRPN Network can be used for target tracking of the video to be recognized, wherein the Network consists of a Siamese Network and a Region pro-positive Network, the former is used for extracting features, and the latter is used for generating candidate regions.

And step 122, correcting the initial running track to obtain the final running track of the target vehicle.

In this embodiment, the manner of correcting the initial travel track may be: carrying out target identification on a video frame in a video to be identified to obtain first characteristic information of a target vehicle; acquiring second characteristic information of the target vehicle in each video frame corresponding to the initial driving track; and reserving the video frame of which the second characteristic information is matched with the first characteristic information.

The characteristic information may include a color of the vehicle, a license plate number, a brand identifier of the vehicle, and the like. Specifically, because the SiamRPN network has uncertainty, an erroneous target may be generated in the target tracking process, and the tracking result may be corrected by using a target detection algorithm centret network. For example, target detection may be performed on the initial driving track every 4 frames to obtain characteristic information of a target vehicle therein, and the characteristic information is compared with the characteristic information of the target vehicle in the video to be identified, if matching, the target tracking is correct, and the video frame is retained; if not, the tracking result is wrong, and the video frame is deleted. The corrected travel track can be used as the final travel track of the target vehicle.

And step 123, adjusting the detection frame in each video frame according to a set proportion.

Specifically, the travel track is formed by a detection frame of the target vehicle in each video frame. In practical applications, the detection frame of the target vehicle may be too large or too small, so that adjustment is required, for example, if the detection frame is too small, the length and width of the detection frame may be respectively enlarged by 5%, so that the target vehicle can be completely presented in the detection frame. Or adjust the detection boxes in each video frame to a uniform size, for example: 224*224. And integrating the corrected and adjusted video frames, and setting a video encoder to obtain the target video.

And step 124, splicing the adjusted video frames to obtain a target video.

Specifically, after the detection frames in the video frames are adjusted to a proper size, the video frames are spliced, so that a target video corresponding to the continuous running of the target vehicle is obtained.

The method comprises the steps of firstly correcting an initial driving track to obtain a final driving track of a target vehicle; then correcting the initial running track to obtain the final running track of the target vehicle; adjusting the detection frames in the video frames according to a set proportion; and finally, splicing the adjusted video frames to obtain a target video. According to the method for obtaining the target video, which is disclosed by the embodiment of the invention, the target video corresponding to the continuous running of the target vehicle can be obtained by tracking, correcting, adjusting and splicing the target to be identified so as to be convenient for the next processing of the target video.

EXAMPLE III

Fig. 4 is a schematic structural diagram of a device for determining an illegal lane change of a vehicle according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: the system comprises a to-be-identified video obtaining module 210, a target video obtaining module 220, a video clip obtaining module 230, a driving category obtaining module 240 and an illegal behavior judging module 250.

The to-be-identified video acquiring module 210 is configured to pre-process an original video to acquire a to-be-identified video.

Optionally, the to-be-identified video obtaining module 210 is further configured to:

segmenting an original video to obtain a plurality of sub-videos; screening a plurality of sub-videos to obtain sub-videos meeting conditions; and splicing the screened sub-videos to obtain the video to be identified.

And the target video obtaining module 220 is configured to determine a running track of the target vehicle in the video to be identified, and obtain a target video corresponding to continuous running of the target vehicle.

Optionally, the target video obtaining module 220 is further configured to:

carrying out target tracking on a video to be identified to obtain an initial driving track; and correcting the initial running track to obtain the final running track of the target vehicle.

Optionally, the target video obtaining module 220 is further configured to:

carrying out target identification on a video frame in a video to be identified to obtain first characteristic information of a target vehicle; acquiring second characteristic information of the target vehicle in each video frame corresponding to the initial driving track; and reserving the video frame of which the second characteristic information is matched with the first characteristic information.

Optionally, the target video obtaining module 220 is further configured to:

adjusting the detection frame in each video frame according to a set proportion; and splicing the adjusted video frames to obtain a target video.

The video segment obtaining module 230 is configured to segment the target video to obtain at least one video segment.

Optionally, the video clip obtaining module 230 is further configured to:

The driving category obtaining module 240 is configured to classify at least one video segment, and obtain a driving category corresponding to each video segment.

Optionally, the driving category obtaining module 240 is further configured to:

and inputting at least one video clip into a set classification model to obtain the driving categories corresponding to the video clips. Wherein the travel category includes: and changing the lane by pressing a dotted line, and changing the lane and not changing the lane by pressing a compacted line.

And the illegal behavior determination module 250 is used for determining that the target vehicle has illegal lane change behavior if the set type exists in the driving types.

Optionally, the illegal behavior determination module 250 is further configured to:

and if the compaction line lane change exists in the driving category corresponding to each video clip, the target vehicle has illegal lane change behaviors.

Example four

Fig. 5 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 5 illustrates a block diagram of a computer device 312 suitable for use in implementing embodiments of the present invention. The computer device 312 shown in FIG. 5 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention. The device 312 is a typical determination calculation device of a lane-change violation of the vehicle.

As shown in FIG. 5, computer device 312 is in the form of a general purpose computing device. The components of computer device 312 may include, but are not limited to: one or more processors 316, a storage device 328, and a bus 318 that couples the various system components including the storage device 328 and the processors 316.

Bus 318 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

Computer device 312 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 312 and includes both volatile and nonvolatile media, removable and non-removable media.

Storage 328 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 330 and/or cache Memory 332. The computer device 312 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 334 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk-Read Only Memory (CD-ROM), a Digital Video disk (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 318 by one or more data media interfaces. Storage 328 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

Program 336 having a set (at least one) of program modules 326 may be stored, for example, in storage 328, such program modules 326 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which may comprise an implementation of a network environment, or some combination thereof. Program modules 326 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

The computer device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing device, camera, display 324, etc.), with one or more devices that enable a user to interact with the computer device 312, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 312 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 322. Also, computer device 312 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), etc.) and/or a public Network, such as the internet, via Network adapter 320. As shown, network adapter 320 communicates with the other modules of computer device 312 via bus 318. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 312, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) systems, tape drives, and data backup storage systems, to name a few.

The processor 316 executes various functional applications and data processing by executing programs stored in the storage device 328, for example, to implement the method for determining a lane change violation of a vehicle according to the above-described embodiment of the present invention.

EXAMPLE five

An embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program that, when executed by a processing device, implements a method of determining a lane change violation of a vehicle as in an embodiment of the present invention. The computer readable medium of the present invention described above may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: preprocessing an original video to obtain a video to be identified; determining a running track of a target vehicle in a video to be identified, and obtaining a target video corresponding to continuous running of the target vehicle; segmenting a target video to obtain at least one video segment; classifying at least one video clip to obtain a driving category corresponding to each video clip; and if the set type exists in the driving types, the target vehicle has illegal lane changing behaviors.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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 case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including 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 using an Internet service provider).

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 the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining an illegal lane change of a vehicle, comprising:

preprocessing an original video to obtain a video to be identified;

segmenting the target video to obtain at least one video segment;

2. The method of claim 1, wherein preprocessing the original video to obtain the video to be recognized comprises:

segmenting an original video to obtain a plurality of sub-videos;

screening the plurality of sub-videos to obtain sub-videos meeting conditions;

and splicing the screened sub-videos to obtain the video to be identified.

3. The method according to claim 1, wherein determining a driving track of a target vehicle in the video to be identified and obtaining a target video corresponding to continuous driving of the target vehicle comprises:

4. The method of claim 3, wherein correcting the initial driving trajectory comprises:

5. The method according to claim 1, wherein the driving track is formed by a detection frame of a target vehicle in each video frame, and obtaining a target video corresponding to continuous driving of the target vehicle comprises:

and splicing the adjusted video frames to obtain a target video.

6. The method of claim 1, wherein segmenting the target video to obtain at least one video segment comprises:

7. The method according to claim 1, wherein the classifying the at least one video clip to obtain the driving category corresponding to each video clip comprises:

8. A device for determining an illegal lane change of a vehicle, comprising:

9. A computer device, the device comprising: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for determining a lane change violation of a vehicle according to any of claims 1-7 when executing the program.

10. A computer-readable storage medium on which a computer program is stored, characterized in that the program, when executed by a processing device, implements the method for determining a vehicle illegal lane change according to any one of claims 1 to 7.