CN114550141A - Vehicle identification system and method - Google Patents
Vehicle identification system and method Download PDFInfo
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- CN114550141A CN114550141A CN202210216934.3A CN202210216934A CN114550141A CN 114550141 A CN114550141 A CN 114550141A CN 202210216934 A CN202210216934 A CN 202210216934A CN 114550141 A CN114550141 A CN 114550141A
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Abstract
The application discloses vehicle identification system and method, its system includes customer end and server, wherein: the client is used for setting a control region and parameters, managing the authority and receiving event messages pushed by the server; the server is used for acquiring the setting parameters of the client, collecting vehicle information in the control area, determining the displacement of the target frame image according to the collected vehicle information, generating an event message according to the displacement and sending the event message to the client. The method and the device can improve the identification accuracy, effectively solve the problem of high flow congestion and greatly reduce the cost.
Description
Technical Field
The application belongs to the technical field of images, and particularly relates to a vehicle identification system and method.
Background
The rapid development of artificial intelligence and computer image technology provides a larger display stage for the application of various industries, especially the rapid and rapid development is achieved in the intelligent transportation field, for example, the widely popularized ETC application in recent years makes a great contribution to intelligent transportation management, the ETC realizes a full-automatic charging system with the function of vehicle non-stop payment parking fee, along with the maturity of the ETC technology, the application is not limited to high-speed charging, and the ETC can continuously expand the application scene to multiple aspects of urban service, such as the services of parking lots, gas stations, vehicle maintenance and the like, and forms the basis of intelligent transportation and intelligent cities.
The premise that various applications based on ETC can well play a role is that the ETC is required to accurately identify the information of vehicles, but at present, under the policy of charging vehicles according to vehicle types on a highway, the inconsistent vehicle types and actual vehicle types are handled when a plurality of vehicles handle ETC, so that a large vehicle logo is caused, a road company is caused to charge according to a trolley type rate, and toll loss is generated.
And the traffic flow of the expressway is particularly large, the maximum traffic flow of 5 thousands of vehicles passing through 1 3 lane portal frames can be achieved, and the condition of large-flow congestion often occurs. Therefore, it is necessary to automatically identify the vehicle type, process the information in time and inform the operator.
Disclosure of Invention
In order to solve the technical problem, the technical scheme of the application is realized as follows:
a vehicle identification system comprises a client and a server, wherein:
the client is used for setting a control region and parameters, managing the authority and receiving event messages pushed by the server;
the server is used for acquiring the setting parameters of the client, collecting vehicle information in the control area, determining the displacement of the target frame image according to the collected vehicle information, generating an event message according to the displacement and sending the event message to the client.
Further, the server comprises a splicing unit, an analysis unit and a storage unit, wherein:
the splicing unit is used for acquiring image information of the vehicle head camera, the vehicle body camera and the vehicle tail camera, analyzing and splicing the image information and generating a data stream of the vehicle.
The analysis unit is used for analyzing the data stream of the vehicle to obtain structured vehicle information;
the storage unit is used for storing information set by the client and vehicle information, generating an event message and sending the event message to the client.
The application also provides a vehicle identification method, which is applied to a vehicle identification system and comprises the following steps:
the client sets a control region and parameters, manages the authority and receives event messages pushed by the server;
the server side obtains the setting parameters of the client side, vehicle information is collected in a control area, the displacement of the target frame image is determined according to the collected vehicle information, and an event message is generated according to the displacement and sent to the client side.
Further, the server side obtains the setting parameters of the client side, vehicle information is collected in a control area, the displacement of the target frame image is determined according to the collected vehicle information, and the step of generating the event message according to the displacement and sending the event message to the client side specifically comprises the following steps:
the splicing unit acquires image information of the vehicle head camera, the vehicle body camera and the vehicle tail camera, analyzes and splices the image information and generates a data stream of the vehicle;
the analysis unit analyzes the data stream of the vehicle to obtain structured vehicle information;
the storage unit stores information set by the client and vehicle information, generates an event message and sends the event message to the client.
Further, the acquiring, by the stitching unit, the image information of the vehicle head camera includes:
acquiring a vehicle head video stream, and identifying the video stream frame by frame;
setting the characteristics of the lane as background characteristics;
and comparing each frame of the vehicle head video stream as the current characteristic and the background characteristic of the vehicle head, and when the characteristic is abnormal, carrying out image processing algorithm recognition on all collected abnormal characteristic graphs to recognize the license plate number and the license plate color of the vehicle.
Further, the acquiring of the image information of the vehicle body camera by the stitching unit includes:
acquiring a vehicle body video stream, and identifying the video stream frame by frame;
setting the characteristics of the lane as background characteristics;
and comparing each frame of the vehicle body video stream as the current characteristic and the background characteristic of the vehicle body, and collecting all images with abnormal characteristics when the characteristic is found to be abnormal, and splicing the images into a complete vehicle body image.
Further, the step of splicing the complete vehicle body image comprises:
intercepting vehicles in the deployment and control area;
fusing the difference between two adjacent frames and the difference between the frames into a three-channel image;
the neural network carries out feature extraction to obtain displacement;
splicing all the pictures according to the displacement;
fusing the coincident elements to obtain a complete car body picture;
and carrying out classification and identification on the complete vehicle body picture to obtain vehicle body image information.
Further, the neural network is mobilenetv 3.
The present invention also discloses an electronic device, comprising: the vehicle identification system comprises a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, when an electronic device runs, the processor is communicated with the storage medium through the bus, and the processor executes the machine-readable instructions to execute the vehicle identification method.
The invention also discloses a storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the vehicle identification method as described.
The vehicle identification system, method and device have the following beneficial effects: according to the method and the system, the distribution control area is set at the client, image acquisition is carried out on the appointed area, key information can be acquired, a large amount of broadband flow and calculation and analysis resources are saved, the efficiency and speed of information processing can be greatly improved, the identification accuracy can be improved, the large-flow congestion condition can be effectively solved, and meanwhile, the cost can be greatly reduced.
Drawings
Fig. 1 is a schematic structural diagram of an embodiment of the present application.
Fig. 2 is a schematic diagram illustrating vehicle body deployment control according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings and embodiments thereof.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an embodiment of the present application, which discloses a vehicle identification system, including a client and a server, wherein:
the client is used for setting a control region and parameters, managing the authority and receiving the event message pushed by the server, and also comprises data query, authority management, event message processing and the like.
The server is used for acquiring the setting parameters of the client, collecting vehicle information in the control area, determining the displacement of the target frame image according to the collected vehicle information, generating an event message according to the displacement and sending the event message to the client.
According to the method and the system, the distribution control area is set at the client, image acquisition is carried out on the appointed area, key information can be acquired, a large amount of broadband flow and calculation and analysis resources are saved, the efficiency and speed of information processing can be greatly improved, the identification accuracy can be improved, the large-flow congestion condition can be effectively solved, and meanwhile, the cost can be greatly reduced.
The corresponding vehicle identification system comprises the following steps:
s1, the client sets the control area and parameters, manages the authority and receives the event message from the server;
and S2, the server side acquires the setting parameters of the client side, collects the vehicle information in the control area, determines the displacement of the target frame image according to the collected vehicle information, generates an event message according to the displacement and sends the event message to the client side.
The step S2 specifically includes:
the splicing unit acquires image information of the vehicle head camera, the vehicle body camera and the vehicle tail camera, analyzes and splices the image information, and generates a data stream of the vehicle.
The analysis unit analyzes the data stream of the vehicle to obtain structured vehicle information;
the storage unit stores information set by the client and vehicle information, generates an event message and sends the event message to the client.
In order to embody the technical solution and the technical effects of the present application, the following description will be further made in conjunction with various preferred embodiments.
As a preferred embodiment, the server includes a splicing unit, an analysis unit, and a storage unit.
The splicing unit is used for acquiring image information of the vehicle head camera, the vehicle body camera and the vehicle tail camera, analyzing and splicing the image information, and generating a data stream of a vehicle. In the application, taking a video stream as an example, a certain condition is also required for acquiring the video stream, and a vehicle head camera and a vehicle tail camera are required to provide a real-time video stream of 25 frames/s at least, and a vehicle body camera provides a real-time video stream of 60 frames/s, so that effective identification can be performed. The camera is required to support the required frame rate. Of course as other alternative embodiments. For example, taking a picture stored by using a camera, and then processing and analyzing the picture also belong to the scope of protection of the image information described in the present application.
The splicing unit is connected to the real-time video stream of the head camera and mainly aims to obtain the license plate number and the license plate color of the vehicle. As an implementation manner, the method and the steps for acquiring the image information are as follows:
after the engine acquires the head video stream, the head video stream is identified frame by frame. If no vehicle exists in the lane, the engine sets the lane characteristics as background characteristics, compares each frame of the vehicle head video stream as current characteristics with the background characteristics, and when the characteristics are abnormal, performs image processing algorithm recognition on all collected abnormal characteristic graphs to recognize the license plate number and the license plate color of the vehicle.
In addition, in order to further improve the accuracy, the image information of the vehicle rear camera needs to be acquired, in order to prevent the license plate of the vehicle head from having obstacles or prevent the algorithm identification from being inaccurate due to irresistible force factors such as foggy days, rainy days and the like, the real-time video stream of the vehicle rear camera is also accessed into the engine, and the purpose is to improve the accuracy of the engine in identifying the license plate number and the license plate color.
The situation of the car body is very complicated, some car bodies are longer due to the limitation of the car body cameras, and the recording width of the video stream cannot cover the whole car body. And if all the records are recorded, not only a large amount of flow is wasted, but also the complexity and the efficiency of calculation are increased.
Therefore, the present application proposes a preferred implementation method for the case of large flow, which includes the steps of:
acquiring a vehicle body video stream, and identifying the video stream frame by frame;
setting the characteristics of the lane as background characteristics;
and comparing each frame of the vehicle body video stream as the current characteristic and the background characteristic of the vehicle body, and collecting all images with abnormal characteristics when the abnormal characteristics are found, and splicing the images into a complete vehicle body image.
As a preferred embodiment, a control distribution area is set, only the vehicle body video stream of the control distribution area is intercepted, and a complete vehicle body image is spliced by adopting a predictive displacement algorithm, wherein the algorithm implementation step comprises the following steps:
intercepting vehicles in the deployment and control area;
fusing the difference between two adjacent frames and the difference between the frames into a three-channel image;
the neural network carries out feature extraction to obtain displacement, namely the displacement of the target frame image;
splicing all the pictures according to the displacement;
fusing the coincident elements to obtain a complete car body picture;
and finally, identifying the complete vehicle body picture by using a classification algorithm, and extracting appearance characteristics in the picture, such as the number of vehicle axles and special vehicle characteristics (for example, explosive marks exist on the tank truck). The algorithm pushes all the identification data to the analysis unit, and the analysis unit analyzes the data stream of the vehicle to obtain the structured vehicle information and sends the structured vehicle information to the storage unit. The storage unit is used for storing information set by the client and vehicle information, generating an event message and sending the event message to the client. And finally, displaying and processing the data by the client.
Referring to fig. 2, in the present application, the setting of deployment and the information of the deployment region setting include the setting of ROI (region of interest), splicing direction, frame rate, exposure setting, and the like. The size of the region set by the ROI can be adjusted according to actual conditions, so that the adjustment can be effectively carried out according to the current network environment or traffic flow. And identifying the established ROI area, and neglecting the rest areas so as to improve the algorithm identification precision and save the calculation power consumption.
According to the characteristics of the technical scheme and the aim of the invention to be realized, in the process of realizing the pair by the displacement prediction algorithm, the neural network is mobilenetv 3.
An embodiment of the present application further provides an electronic device, where the electronic device includes a processor and a memory, where the memory stores an executable program, and when the executable program runs on a computer, the computer executes the vehicle identification method according to any of the above embodiments.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in this embodiment are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present application are only for distinguishing the modules or steps, and are not to be construed as an improper limitation of the present application.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present application have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present application is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the present application, and the technical scheme after the changes or substitutions will fall into the protection scope of the present application.
Claims (10)
1. A vehicle identification system is characterized by comprising a client and a server, wherein:
the client is used for setting a control region and parameters, managing the authority and receiving event messages pushed by the server;
the server is used for acquiring the setting parameters of the client, collecting vehicle information in the control area, determining the displacement of the target frame image according to the collected vehicle information, generating an event message according to the displacement and sending the event message to the client.
2. The vehicle identification system of claim 1, wherein the server comprises a concatenation unit, an analysis unit, and a storage unit, wherein:
the splicing unit is used for acquiring image information of the vehicle head camera, the vehicle body camera and the vehicle tail camera, analyzing and splicing the image information and generating a data stream of the vehicle;
the analysis unit is used for analyzing the data stream of the vehicle to obtain structured vehicle information;
the storage unit is used for storing information set by the client and vehicle information, generating an event message and sending the event message to the client.
3. A vehicle identification method is applied to a vehicle identification system and is characterized by comprising the following steps:
the client sets a control region and parameters, manages the authority and receives event messages pushed by the server;
the server side obtains the setting parameters of the client side, vehicle information is collected in a control area, the displacement of the target frame image is determined according to the collected vehicle information, and an event message is generated according to the displacement and sent to the client side.
4. The vehicle identification method according to claim 3, wherein the server acquires the setting parameters of the client, collects the vehicle information in the deployment and control area, determines the displacement of the target frame image according to the collected vehicle information, and generates an event message according to the displacement to send to the client specifically comprises the steps of:
the splicing unit acquires image information of the vehicle head camera, the vehicle body camera and the vehicle tail camera, analyzes and splices the image information and generates a data stream of the vehicle;
the analysis unit analyzes the data stream of the vehicle to obtain structured vehicle information;
the storage unit stores information set by the client and vehicle information, generates an event message and sends the event message to the client.
5. The vehicle identification method of claim 4, wherein the stitching unit acquiring the image information of the head camera comprises:
acquiring a vehicle head video stream, and identifying the video stream frame by frame;
setting the characteristics of the lane as background characteristics;
and comparing each frame of the vehicle head video stream as the current characteristic and the background characteristic of the vehicle head, and when the characteristic is abnormal, carrying out image processing algorithm recognition on all collected abnormal characteristic graphs to recognize the license plate number and the license plate color of the vehicle.
6. The vehicle identification method of claim 4, wherein the stitching unit acquiring the image information of the body camera comprises:
acquiring a vehicle body video stream, and identifying the video stream frame by frame;
setting the characteristics of the lane as background characteristics;
and comparing each frame of the vehicle body video stream as the current characteristic and the background characteristic of the vehicle body, and collecting all images with abnormal characteristics when the characteristic is found to be abnormal, and splicing the images into a complete vehicle body image.
7. The vehicle identification method of claim 6, wherein the step of stitching together the complete body image comprises:
intercepting vehicles in the deployment and control area;
fusing the difference between two adjacent frames and the difference between the frames into a three-channel image;
the neural network carries out feature extraction to obtain displacement;
splicing all the pictures according to the displacement;
fusing the coincident elements to obtain a complete car body picture;
and carrying out classification and identification on the complete vehicle body picture to obtain vehicle body image information.
8. The vehicle identification method according to claim 7, wherein the neural network is mobilenetv 3.
9. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the vehicle identification method according to any one of claims 3 to 8.
10. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, performs the vehicle identification method according to any one of claims 3 to 8.
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| CN202210216934.3A CN114550141A (en) | 2022-03-07 | 2022-03-07 | Vehicle identification system and method |
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| CN202210216934.3A CN114550141A (en) | 2022-03-07 | 2022-03-07 | Vehicle identification system and method |
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| CN113962864A (en) * | 2021-11-12 | 2022-01-21 | 上海闪马智能科技有限公司 | Image splicing method and device, storage medium and electronic device |
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- 2022-03-07 CN CN202210216934.3A patent/CN114550141A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107038683A (en) * | 2017-03-27 | 2017-08-11 | 中国科学院自动化研究所 | The method for panoramic imaging of moving target |
| CN111860384A (en) * | 2020-07-27 | 2020-10-30 | 上海福赛特智能科技有限公司 | Vehicle type recognition method |
| CN111862623A (en) * | 2020-07-27 | 2020-10-30 | 上海福赛特智能科技有限公司 | Vehicle side map splicing device and method |
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