Electric vehicle violation identification method, device, equipment and storage medium

Abstract

The present application is applicable to the field of data processing technology, and provides a method, device, equipment and storage medium for identifying traffic violations of electric vehicles. The method includes: obtaining a set of images of a target electric vehicle collected by cameras at both ends of a road, wherein the two ends of the road include a first end of the road and a second end of the road, and the target electric vehicle travels from the first end of the road to the second end of the road; extracting the license plate information of the target electric vehicle based on the image set, and extracting the motion trajectory of the target electric vehicle collected by the camera at the first end of the road and the motion trajectory of the target electric vehicle collected by the camera at the second end of the road, which are respectively recorded as the first motion trajectory and the second motion trajectory; determining whether the target electric vehicle has committed a traffic violation based on the first motion trajectory, the second motion trajectory, the non-motor vehicle lane area and the license plate information. The present application can accurately and timely warn traffic violators and reduce the occurrence of traffic violations by electric vehicles.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular relates to methods, devices, equipment and storage media for identifying violations of electric vehicles. Background Art

[0002] With the advancement of science and technology and the improvement of people's living standards, the number of private cars has increased significantly. The popularization of cars has greatly facilitated people's daily travel. At the same time, it has also put greater pressure on the load capacity of the road traffic system, especially during rush hours such as commuting, which can easily cause traffic jams. As a result, most people currently choose to travel by electric bikes. Because electric bikes are easy to operate and do not require traffic jams, they have become rapidly popular as a means of travel.

[0003] However, with the widespread popularity of electric vehicles, a large number of traffic violations have also increased. In particular, after the people involved commit traffic violations, it is difficult to get corresponding warnings, which prompts some people to frequently commit traffic violations, especially when driving on motor vehicle lanes, which brings great safety hazards to themselves and traffic management. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, equipment and storage medium for identifying electric vehicle traffic violations, so as to accurately and promptly warn traffic violators and reduce the occurrence of electric vehicle traffic violations.

[0005] This application is achieved through the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for identifying violations of electric vehicle regulations, comprising:

[0007] A set of images of a target electric vehicle captured by cameras at both ends of a road is obtained, wherein the two ends of the road include a first end of the road and a second end of the road, and the target electric vehicle travels from the first end of the road to the second end of the road.

[0008] Based on the image set, the license plate information of the target electric vehicle is extracted, and the motion trajectory of the target electric vehicle captured by the camera at the first end of the road and the motion trajectory of the target electric vehicle captured by the camera at the second end of the road are extracted, and recorded as the first motion trajectory and the second motion trajectory respectively.

[0009] Based on the first motion trajectory, the second motion trajectory, the non-motorized vehicle lane area and the license plate information, it is determined whether the target electric vehicle has committed any traffic violation.

[0010] In conjunction with the first aspect, in some possible implementations, determining whether the target electric vehicle has violated traffic regulations based on the first motion trajectory, the second motion trajectory, the non-motorized vehicle lane area, and the license plate information includes:

[0011] According to the first motion trajectory, a motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road is predicted, and recorded as the first predicted motion trajectory.

[0012] According to the second motion trajectory, the motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road is predicted, and recorded as the second predicted motion trajectory.

[0013] If the first predicted motion trajectory intersects with the second predicted motion trajectory, the first predicted motion trajectory before the intersection, the second predicted motion trajectory after the intersection, the first motion trajectory and the second motion trajectory are taken to obtain a complete motion trajectory of the target electric vehicle.

[0014] If the entire movement trajectory is entirely within the non-motorized vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior.

[0015] If there is a part of the complete motion trajectory that is outside the non-motorized vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations.

[0016] If the first predicted motion trajectory does not intersect with the second predicted motion trajectory, the number of traffic violations that the target electric vehicle has committed within a preset time period is determined based on the license plate information.

[0017] If the number of traffic violation behaviors occurring within a preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a traffic violation.

[0018] If the number of traffic violation behaviors within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any traffic violation.

[0019] In conjunction with the first aspect, in some possible implementations, the method further includes:

[0020] If the first predicted motion trajectory and the second predicted motion trajectory do not intersect, it is determined whether the first predicted motion trajectory and the second predicted motion trajectory are both within the non-motor vehicle lane area.

[0021] If the first predicted motion trajectory is within the non-motor vehicle lane area and the second predicted motion trajectory is within the non-motor vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior.

[0022] If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of violations within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any violations.

[0023] If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of violations within a preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a violation.

[0024] If the first predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations.

[0025] In conjunction with the first aspect, in some possible implementations, predicting, based on the first motion trajectory, a motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road, recorded as the first predicted motion trajectory, includes:

[0026] Based on the first motion trajectory, a motion direction and a motion speed of the target electric vehicle in the first motion trajectory are obtained. Based on the motion direction and the motion speed of the target electric vehicle in the first motion trajectory, a first predicted motion trajectory is obtained.

[0027] Based on the second motion trajectory, the motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road is predicted, which is recorded as the second predicted motion trajectory, including:

[0028] Based on the second motion trajectory, a motion direction and a motion speed of the target electric vehicle in the second motion trajectory are obtained. Based on the motion direction and the motion speed of the target electric vehicle in the second motion trajectory, a second predicted motion trajectory is obtained.

[0029] In conjunction with the first aspect, in some possible implementations, extracting a motion trajectory of a target electric vehicle captured by a camera at a first end of the road and a motion trajectory of the target electric vehicle captured by a camera at a second end of the road, respectively recorded as a first motion trajectory and a second motion trajectory, includes:

[0030] The image set is divided into an image set of a first end of the road and an image set of a second end of the road.

[0031] Based on the image set at the first end of the road, a target electric vehicle is determined, and the transparency of the images in the image set at the first end of the road is adjusted and overlapped, and the center points of multiple target electric vehicles in the overlapped images are connected to obtain a first motion trajectory.

[0032] Based on the image set at the second end of the road, the target electric vehicle is determined, and the transparency of the images in the image set at the second end of the road is adjusted and overlapped, and the center points of multiple target electric vehicles in the overlapped images are connected to obtain a second motion trajectory.

[0033] In conjunction with the first aspect, in some possible implementations, extracting the license plate information of the target electric vehicle based on the image set includes:

[0034] The license plate image of the target electric vehicle in each image in the image set is obtained respectively to obtain multiple license plate images.

[0035] Information extraction is performed on multiple license plate images in sequence to obtain multiple information extraction results.

[0036] For each word in the license plate information of the target electric vehicle, the corresponding word in the multiple information extraction results is processed based on the fuzzy algorithm to obtain the output result of the word;

[0037] Summarize the output results of each word to obtain the license plate information of the target electric vehicle.

[0038] In conjunction with the first aspect, in some possible implementations, the method further includes:

[0039] Record the traffic violations of the target electric vehicle.

[0040] Based on the license plate information, obtain the contact information of the license plate information registration.

[0041] Based on the contact information, the driver of the target electric vehicle is notified of his or her illegal behavior.

[0042] In a second aspect, an embodiment of the present application provides a device for identifying violations of electric vehicles, comprising:

[0043] The image acquisition module is used to acquire a set of images of the target electric vehicle captured by cameras at both ends of the road, wherein the two ends of the road include a first end of the road and a second end of the road, and the target electric vehicle travels from the first end of the road to the second end of the road.

[0044] The trajectory confirmation module is used to extract the license plate information of the target electric vehicle based on the image set, and extract the motion trajectory of the target electric vehicle captured by the camera at the first end of the road and the motion trajectory of the target electric vehicle captured by the camera at the second end of the road, which are recorded as the first motion trajectory and the second motion trajectory respectively.

[0045] The result output module is used to determine whether the target electric vehicle has violated traffic regulations based on the first motion trajectory, the second motion trajectory, the non-motor vehicle lane area and the license plate information.

[0046] In a third aspect, an embodiment of the present application provides a terminal device comprising: a processor and a memory, the memory being used to store a computer program, and the processor implementing the electric vehicle violation behavior identification method as described in any one of the first aspects when executing the computer program.

[0047] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for identifying electric vehicle violation behaviors as described in any one of the first aspects is implemented.

[0048] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0049] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0050] This application extracts the first motion trajectory, the second motion trajectory and the license plate information of the target electric vehicle based on the image set of the target electric vehicle collected by cameras at both ends of the road. Then, it determines whether the target electric vehicle has driven out of the non-motor vehicle lane area by combining the first motion trajectory, the second motion trajectory and the license plate information. It can predict whether there are violations in areas without cameras, and can promptly warn traffic violators after the driver commits a violation, thereby reducing the occurrence of traffic violations by electric vehicles and reducing safety hazards.

[0051] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 This is a flow chart of a method for identifying electric vehicle traffic violation behaviors provided by an embodiment of the present application;

[0054] Figure 2 This is a schematic diagram of the structure of an electric vehicle violation behavior recognition device provided by an embodiment of the present application;

[0055] Figure 3 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0057] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0058] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0059] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0060] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0061] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0062] The present invention provides a method for identifying violations of electric vehicle regulations. Figure 1This is a schematic diagram of the process of the electric vehicle violation behavior identification method provided by an embodiment of the present application, referring to Figure 1 , the detailed description of the electric vehicle violation behavior identification method is as follows:

[0063] Step 101 : obtaining a set of images of a target electric vehicle captured by cameras at both ends of a road, wherein the two ends of the road include a first end of the road and a second end of the road, and the target electric vehicle travels from the first end of the road to the second end of the road.

[0064] In some specific embodiments, the two ends of a road refer to two intersections of the road (intersections where electronic probes are installed).

[0065] Step 102: Based on the image set, extract the license plate information of the target electric vehicle, and extract the motion trajectory of the target electric vehicle captured by the camera at the first end of the road and the motion trajectory of the target electric vehicle captured by the camera at the second end of the road, which are respectively recorded as the first motion trajectory and the second motion trajectory.

[0066] Exemplarily, extracting the license plate information of the target electric vehicle based on the image set may include:

[0067] The license plate image of the target electric vehicle in each image in the image set is obtained respectively to obtain multiple license plate images.

[0068] Information extraction is performed on multiple license plate images in sequence to obtain multiple information extraction results.

[0069] For each word in the license plate information of the target electric vehicle, the corresponding word in the multiple information extraction results is processed based on the fuzzy algorithm to obtain the output result of the word;

[0070] Summarize the output results of each word to obtain the license plate information of the target electric vehicle.

[0071] In some specific embodiments, in order to ensure the accuracy of the extracted license plate information, the license plate image in each image is obtained, and the license plate information in each image is extracted, and then processed by a fuzzy algorithm to obtain the final license plate information. The fuzzy algorithm can filter the most likely one from multiple extracted results as the final output. Corresponding to this solution, such processing is performed on each word, so the final license plate information is more accurate.

[0072] Exemplarily, extracting the motion trajectory of the target electric vehicle captured by a camera at a first end of the road and the motion trajectory of the target electric vehicle captured by a camera at a second end of the road, respectively recorded as a first motion trajectory and a second motion trajectory, may include:

[0073] The image set is divided into an image set of a first end of the road and an image set of a second end of the road.

[0074] Based on the image set at the first end of the road, a target electric vehicle is determined, and the transparency of the images in the image set at the first end of the road is adjusted and overlapped, and the center points of multiple target electric vehicles in the overlapped images are connected to obtain a first motion trajectory.

[0075] Based on the image set at the second end of the road, the target electric vehicle is determined, and the transparency of the images in the image set at the second end of the road is adjusted and overlapped, and the center points of multiple target electric vehicles in the overlapped images are connected to obtain a second motion trajectory.

[0076] Step 103 : determining whether the target electric vehicle has violated traffic regulations based on the first motion trajectory, the second motion trajectory, the non-motorized vehicle lane area, and the license plate information.

[0077] Exemplarily, step 103 may include:

[0078] According to the first motion trajectory, a motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road is predicted, and recorded as the first predicted motion trajectory.

[0079] According to the second motion trajectory, the motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road is predicted, and recorded as the second predicted motion trajectory.

[0080] If the first predicted motion trajectory intersects with the second predicted motion trajectory, the first predicted motion trajectory before the intersection, the second predicted motion trajectory after the intersection, the first motion trajectory and the second motion trajectory are taken to obtain a complete motion trajectory of the target electric vehicle.

[0081] If the entire movement trajectory is entirely within the non-motorized vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior.

[0082] If there is a part of the complete motion trajectory that is outside the non-motorized vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations.

[0083] If the first predicted motion trajectory does not intersect with the second predicted motion trajectory, the number of traffic violations that the target electric vehicle has committed within a preset time period is determined based on the license plate information.

[0084] If the number of traffic violation behaviors occurring within a preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a traffic violation.

[0085] If the number of traffic violation behaviors within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any traffic violation.

[0086] In some specific embodiments, to obtain a more accurate trajectory of the target electric vehicle when not being captured by the camera, a first predicted trajectory and a second predicted trajectory are combined. When the two predicted trajectories intersect, it is determined that the target electric vehicle's movement is likely to overlap with a portion of the two predicted trajectories. At the intersection, the target electric vehicle enters the second predicted trajectory. The first predicted trajectory before the intersection, the second predicted trajectory after the intersection, and the first and second predicted trajectories are then connected to obtain the target electric vehicle's complete trajectory. Based on the complete trajectory, it is then possible to determine whether the target electric vehicle has left the non-motorized vehicle lane.

[0087] Exemplarily, the method may further include:

[0088] If the first predicted motion trajectory and the second predicted motion trajectory do not intersect, it is determined whether the first predicted motion trajectory and the second predicted motion trajectory are both within the non-motor vehicle lane area.

[0089] If the first predicted motion trajectory is within the non-motor vehicle lane area and the second predicted motion trajectory is within the non-motor vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior.

[0090] If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of violations within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any violations.

[0091] If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of violations within a preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a violation.

[0092] If the first predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations.

[0093] In some specific embodiments, the number of traffic violations occurring within a preset time period reflects the driver's historical traffic violations. Based on this situation, the possibility of this violation can be inferred, and whether a traffic violation has occurred can be determined more accurately.

[0094] Exemplarily, based on the first motion trajectory, predicting the motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road, recorded as the first predicted motion trajectory, may include:

[0095] Based on the first motion trajectory, a motion direction and a motion speed of the target electric vehicle in the first motion trajectory are obtained. Based on the motion direction and the motion speed of the target electric vehicle in the first motion trajectory, a first predicted motion trajectory is obtained.

[0096] Predicting the motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road based on the second motion trajectory, recorded as the second predicted motion trajectory, may include:

[0097] Based on the second motion trajectory, a motion direction and a motion speed of the target electric vehicle in the second motion trajectory are obtained. Based on the motion direction and the motion speed of the target electric vehicle in the second motion trajectory, a second predicted motion trajectory is obtained.

[0098] Exemplarily, the electric vehicle violation identification method may further include:

[0099] Record the traffic violations of the target electric vehicle.

[0100] Based on the license plate information, obtain the contact information of the license plate information registration.

[0101] Based on the contact information, the driver of the target electric vehicle is notified of his or her illegal behavior.

[0102] The above-mentioned electric vehicle violation identification method extracts the first motion trajectory, the second motion trajectory and the license plate information of the target electric vehicle based on the image set of the target electric vehicle collected by the cameras at both ends of the road. Then, it judges whether the target electric vehicle has driven out of the non-motor vehicle lane area by combining the first motion trajectory, the second motion trajectory and the license plate information. It can predict whether violations occur in areas without cameras, and can promptly warn traffic violators after the driver commits a violation, thereby reducing the occurrence of electric vehicle traffic violations and reducing safety hazards.

[0103] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0104] Corresponding to the electric vehicle violation behavior identification method described in the above embodiment, Figure 2 A structural block diagram of an electric vehicle violation behavior identification device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0105] See also Figure 2 The electric vehicle violation behavior recognition device in the embodiment of the present application may include:

[0106] The image acquisition module 201 is used to acquire a set of images of the target electric vehicle captured by cameras at both ends of the road, wherein the two ends of the road include a first end of the road and a second end of the road, and the target electric vehicle travels from the first end of the road to the second end of the road.

[0107] The trajectory confirmation module 202 is used to extract the license plate information of the target electric vehicle based on the image set, and extract the motion trajectory of the target electric vehicle captured by the camera at the first end of the road and the motion trajectory of the target electric vehicle captured by the camera at the second end of the road, which are recorded as the first motion trajectory and the second motion trajectory respectively.

[0108] The result output module 203 is used to determine whether the target electric vehicle has violated traffic regulations based on the first motion trajectory, the second motion trajectory, the non-motor vehicle lane area, and the license plate information.

[0109] Exemplarily, the result output module 203 may also be used to:

[0110] According to the first motion trajectory, a motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road is predicted, and recorded as the first predicted motion trajectory.

[0111] According to the second motion trajectory, the motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road is predicted, and recorded as the second predicted motion trajectory.

[0112] If the first predicted motion trajectory intersects with the second predicted motion trajectory, the first predicted motion trajectory before the intersection, the second predicted motion trajectory after the intersection, the first motion trajectory and the second motion trajectory are taken to obtain a complete motion trajectory of the target electric vehicle.

[0113] If the entire movement trajectory is entirely within the non-motorized vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior.

[0114] If there is a part of the complete motion trajectory that is outside the non-motorized vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations.

[0115] If the first predicted motion trajectory does not intersect with the second predicted motion trajectory, the number of traffic violations that the target electric vehicle has committed within a preset time period is determined based on the license plate information.

[0116] If the number of traffic violation behaviors occurring within a preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a traffic violation.

[0117] If the number of traffic violation behaviors within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any traffic violation.

[0118] Exemplarily, the result output module 203 may also be used to:

[0119] If the first predicted motion trajectory and the second predicted motion trajectory do not intersect, it is determined whether the first predicted motion trajectory and the second predicted motion trajectory are both within the non-motor vehicle lane area.

[0120] If the first predicted motion trajectory is within the non-motor vehicle lane area and the second predicted motion trajectory is within the non-motor vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior.

[0121] If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of violations within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any violations.

[0122] If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of violations within a preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a violation.

[0123] If the first predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations.

[0124] Exemplarily, the result output module 203 may also be used to:

[0125] Based on the first motion trajectory, a motion direction and a motion speed of the target electric vehicle in the first motion trajectory are obtained. Based on the motion direction and the motion speed of the target electric vehicle in the first motion trajectory, a first predicted motion trajectory is obtained.

[0126] Exemplarily, the result output module 203 may also be used to:

[0127] Based on the second motion trajectory, a motion direction and a motion speed of the target electric vehicle in the second motion trajectory are obtained. Based on the motion direction and the motion speed of the target electric vehicle in the second motion trajectory, a second predicted motion trajectory is obtained.

[0128] Exemplarily, the trajectory confirmation module 202 may also be used to:

[0129] The image set is divided into an image set of a first end of the road and an image set of a second end of the road.

[0130] Based on the image set at the first end of the road, a target electric vehicle is determined, and the transparency of the images in the image set at the first end of the road is adjusted and overlapped, and the center points of multiple target electric vehicles in the overlapped images are connected to obtain a first motion trajectory.

[0131] Based on the image set at the second end of the road, the target electric vehicle is determined, and the transparency of the images in the image set at the second end of the road is adjusted and overlapped, and the center points of multiple target electric vehicles in the overlapped images are connected to obtain a second motion trajectory.

[0132] Exemplarily, the trajectory confirmation module 202 may also be used to:

[0133] The license plate image of the target electric vehicle in each image in the image set is obtained respectively to obtain multiple license plate images.

[0134] Information extraction is performed on multiple license plate images in sequence to obtain multiple information extraction results.

[0135] For each word in the license plate information of the target electric vehicle, the corresponding word in the multiple information extraction results is processed based on the fuzzy algorithm to obtain the output result of the word;

[0136] Summarize the output results of each word to obtain the license plate information of the target electric vehicle.

[0137] Exemplarily, the result output module 203 may also be used to:

[0138] Record the traffic violations of the target electric vehicle.

[0139] Based on the license plate information, obtain the contact information of the license plate information registration.

[0140] Based on the contact information, the driver of the target electric vehicle is notified of his or her illegal behavior.

[0141] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0143] The present application also provides a terminal device. Figure 3 The terminal device 300 may include: at least one processor 310, a memory 320, the memory 320 is used to store a computer program 321, the processor 310 is used to call and run the computer program 321 stored in the memory 320 to implement the steps of any of the above-mentioned method embodiments, for example Figure 1 Steps 101 to 104 in the embodiment shown. Alternatively, when the processor 310 executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, for example Figure 2 The functions of each module are shown.

[0144] Exemplarily, the computer program 321 may be divided into one or more modules / units, one or more of which are stored in the memory 320 and executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments capable of completing specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 300.

[0145] Those skilled in the art will understand that Figure 3 These are merely examples of terminal devices and do not constitute a limitation on the terminal devices. The terminal devices may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0146] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0147] The memory 320 can be an internal storage unit of the terminal device or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 can also be used to temporarily store data that has been output or is about to be output.

[0148] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0149] The electric vehicle violation behavior identification method provided in the embodiment of the present application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptops, netbooks, etc. The embodiment of the present application does not impose any restrictions on the specific type of terminal devices.

[0150] An embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps in each embodiment of the above-mentioned electric vehicle violation identification method can be implemented.

[0151] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned electric vehicle violation behavior identification method when executing the computer program product.

[0152] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0153] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0154] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0156] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0157] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for identifying illegal behavior of electric vehicles, characterized in that: include: Acquire a set of images of a target electric vehicle captured by cameras at two ends of a road, wherein the two ends of the road include a first end of the road and a second end of the road, and the target electric vehicle travels from the first end of the road to the second end of the road; Extracting the license plate information of the target electric vehicle based on the image set, and extracting the motion trajectory of the target electric vehicle captured by a camera at a first end of the road and the motion trajectory of the target electric vehicle captured by a camera at a second end of the road, respectively recorded as a first motion trajectory and a second motion trajectory; Determining whether the target electric vehicle has violated traffic regulations based on the first motion trajectory, the second motion trajectory, the non-motorized vehicle lane area, and the license plate information; Determining whether the target electric vehicle has violated traffic regulations based on the first motion trajectory, the second motion trajectory, the non-motorized vehicle lane area, and the license plate information includes: Predicting, based on the first motion trajectory, a motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road, which is recorded as a first predicted motion trajectory; Predicting, based on the second motion trajectory, a motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road, which is recorded as a second predicted motion trajectory; If the first predicted motion trajectory intersects with the second predicted motion trajectory, the first predicted motion trajectory before the intersection, the second predicted motion trajectory after the intersection, the first motion trajectory, and the second motion trajectory are taken to obtain a complete motion trajectory of the target electric vehicle; If the entire motion trajectory is within the non-motor vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior; If the complete motion trajectory includes a portion outside the non-motor vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations; If the first predicted motion trajectory does not intersect with the second predicted motion trajectory, determining the number of traffic violations that the target electric vehicle has committed within a preset time period based on the license plate information; If the number of traffic violation behaviors within the preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a traffic violation; If the number of traffic violation behaviors occurring within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any traffic violation.

2. The electric vehicle violation identification method according to claim 1, characterized in that: The method further comprises: If the first predicted motion trajectory and the second predicted motion trajectory do not intersect, determining whether the first predicted motion trajectory and the second predicted motion trajectory are both within the non-motorized vehicle lane area; If the first predicted motion trajectory is within the non-motor vehicle lane area, and the second predicted motion trajectory is within the non-motor vehicle lane area, it is determined that the target electric vehicle has not committed any traffic violation; If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of traffic violations within the preset time period does not exceed a first threshold, it is determined that the target electric vehicle has not committed any traffic violations; If either the first predicted motion trajectory or the second predicted motion trajectory has a portion outside the non-motor vehicle lane area, and the number of traffic violations within the preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a traffic violation; If the first predicted motion trajectory includes a portion outside the non-motor vehicle lane area, and the second predicted motion trajectory includes a portion outside the non-motor vehicle lane area, it is determined that the target electric vehicle has committed a traffic violation.

3. The electric vehicle violation identification method according to claim 1, characterized in that: Predicting, based on the first motion trajectory, a motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road, recorded as a first predicted motion trajectory, includes: Based on the first motion trajectory, obtaining a motion direction and a motion speed of the target electric vehicle in the first motion trajectory, and based on the motion direction and the motion speed of the target electric vehicle in the first motion trajectory, obtaining the first predicted motion trajectory; The step of predicting the motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road based on the second motion trajectory, recorded as the second predicted motion trajectory, includes: Based on the second motion trajectory, a motion direction and a motion speed of the target electric vehicle in the second motion trajectory are obtained, and based on the motion direction and the motion speed of the target electric vehicle in the second motion trajectory, the second predicted motion trajectory is obtained.

4. The electric vehicle violation identification method according to claim 1, characterized in that: The extracting the motion trajectory of the target electric vehicle captured by the camera at the first end of the road and the motion trajectory of the target electric vehicle captured by the camera at the second end of the road, respectively recorded as the first motion trajectory and the second motion trajectory, includes: dividing the image set into an image set of a first end of the road and an image set of a second end of the road; Determining the target electric vehicle based on the image set at the first end of the road, adjusting the transparency of images in the image set at the first end of the road and overlapping them, and connecting center points of multiple target electric vehicles in the overlapped images to obtain the first motion trajectory; Based on the image set at the second end of the road, the target electric vehicle is determined, and the transparency of the images in the image set at the second end of the road is adjusted and overlapped, and the center points of multiple target electric vehicles in the overlapped images are connected to obtain the second motion trajectory.

5. The electric vehicle violation identification method according to claim 1, characterized in that: Extracting the license plate information of the target electric vehicle based on the image set includes: Acquire the license plate image of the target electric vehicle in each image in the image set respectively to obtain multiple license plate images; Extracting information from the plurality of license plate images in sequence to obtain a plurality of information extraction results; For each word in the license plate information of the target electric vehicle, processing the corresponding word in the plurality of information extraction results based on a fuzzy algorithm to obtain an output result of the word; The output results of each word are summarized to obtain the license plate information of the target electric vehicle.

6. The electric vehicle violation identification method according to claim 1, characterized in that: The method further comprises: Recording the traffic violation behavior of the target electric vehicle; Based on the license plate information, obtaining contact information for registration of the license plate information; The driver of the target electric vehicle is notified of the traffic violation according to the contact information.

7. An electric vehicle violation behavior recognition device, characterized in that: include: an image acquisition module, configured to acquire a set of images of a target electric vehicle captured by cameras at two ends of a road, wherein the two ends of the road include a first end of the road and a second end of the road, and the target electric vehicle travels from the first end of the road to the second end of the road; a trajectory confirmation module, configured to extract the license plate information of the target electric vehicle based on the image set, and extract the motion trajectory of the target electric vehicle captured by the camera at the first end of the road and the motion trajectory of the target electric vehicle captured by the camera at the second end of the road, respectively recorded as a first motion trajectory and a second motion trajectory; A result output module is used to determine whether the target electric vehicle has violated traffic regulations based on the first motion trajectory, the second motion trajectory, the non-motor vehicle lane area and the license plate information; The result output module is specifically used to: Predicting, based on the first motion trajectory, a motion trajectory of the target electric vehicle after it leaves the acquisition range of the camera at the first end of the road, which is recorded as a first predicted motion trajectory; Predicting, based on the second motion trajectory, a motion trajectory of the target electric vehicle before it enters the acquisition range of the camera at the second end of the road, which is recorded as a second predicted motion trajectory; If the first predicted motion trajectory intersects with the second predicted motion trajectory, the first predicted motion trajectory before the intersection, the second predicted motion trajectory after the intersection, the first motion trajectory, and the second motion trajectory are taken to obtain a complete motion trajectory of the target electric vehicle; If the entire motion trajectory is within the non-motor vehicle lane area, it is determined that the target electric vehicle has not committed any illegal behavior; If the complete motion trajectory includes a portion outside the non-motor vehicle lane area, it is determined that the target electric vehicle has violated traffic regulations; If the first predicted motion trajectory does not intersect with the second predicted motion trajectory, determining the number of traffic violations that the target electric vehicle has committed within a preset time period based on the license plate information; If the number of traffic violation behaviors occurring within the preset time period exceeds a first threshold, it is determined that the target electric vehicle has committed a traffic violation; If the number of traffic violation behaviors occurring within the preset time period does not exceed the first threshold, it is determined that the target electric vehicle has not committed any traffic violation.

8. A terminal device comprising: A processor and a memory, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, it implements the electric vehicle violation behavior identification method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying illegal behavior of an electric vehicle as described in any one of claims 1 to 6 is implemented.

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