WO2020034534A1 - 基于行车记录仪的举证方法、装置、设备和存储介质 - Google Patents
基于行车记录仪的举证方法、装置、设备和存储介质 Download PDFInfo
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- WO2020034534A1 WO2020034534A1 PCT/CN2018/122816 CN2018122816W WO2020034534A1 WO 2020034534 A1 WO2020034534 A1 WO 2020034534A1 CN 2018122816 W CN2018122816 W CN 2018122816W WO 2020034534 A1 WO2020034534 A1 WO 2020034534A1
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- 238000004519 manufacturing process Methods 0.000 title abstract 6
- 238000000034 method Methods 0.000 claims description 47
- 230000007613 environmental effect Effects 0.000 claims description 17
- 230000008569 process Effects 0.000 claims description 16
- 206010039203 Road traffic accident Diseases 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000010191 image analysis Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0841—Registering performance data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
Definitions
- the present application relates to the field of vehicle-mounted equipment, and in particular, to a method, a device, a device, and a storage medium for providing evidence based on a driving recorder.
- the driving recorder is used to record video information of the surrounding environment during driving, that is, the camera of the driving recorder captures the surrounding environment or the interior of the vehicle, and traffic occurs in the vehicle itself.
- the vehicle owner manually retrieves and searches for video information recorded by the driving recorder to provide evidence.
- Such a proof method requires manual operation by the user, which makes the user experience low. How to prove more conveniently and quickly has become a technical problem that needs to be solved urgently.
- the main purpose of this application is to provide a proof method, device, equipment and storage medium based on a driving recorder, which aims to improve the convenience of proof of a vehicle accident.
- the present application provides a driving recorder-based proof method, which includes the following steps:
- the target video image in each frame is used as proof information and sent to a traffic management platform for reporting.
- the present application further provides a driving recorder-based proof device, which is characterized in that the driving recorder-based proof device includes:
- a receiving and acquiring module configured to receive a vehicle accident proof request, and obtain the accident information contained in the vehicle accident proof request;
- the acquisition and recognition module is configured to acquire an accident occurrence time in the accident information, use video information corresponding to the accident occurrence time as accident video information, and input the accident video information into a preset recognition model to obtain a recognition result. ;
- An image acquisition module configured to acquire, according to the recognition result, a target video image of each frame in the accident video information that meets a preset proof rule
- a report report module is configured to send the target video image in each frame as proof information and send the report to a traffic management platform.
- the present application also provides a proof device based on a driving recorder
- the driving recorder-based proof device includes a camera, a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor, wherein:
- the camera is used for shooting and obtaining video information
- the present application also provides a computer storage medium
- Computer-readable instructions are stored on the computer storage medium, and when the computer-readable instructions are executed by a processor, the steps of the method for proofing based on a driving recorder as described above are implemented.
- the embodiment of the present application proposes a proof method, device, equipment and storage medium based on a driving recorder, and obtains the accident information included in the vehicle accident proof request by receiving a vehicle accident proof request; and acquires the time of occurrence of the accident in the accident information.
- the driving recorder can automatically extract the accident video information and send the traffic management platform to report, making it easier to prove the vehicle accident.
- the video information recorded by the driving recorder is used as the accident proof information, which makes the accident proof Reality is high.
- FIG. 1 is a schematic structural diagram of a device for a hardware operating environment involved in a solution according to an embodiment of the present application
- FIG. 2 is a schematic flowchart of a first embodiment of a proof method based on a driving recorder of this application;
- FIG. 3 is a schematic flowchart of a third embodiment of a proof method based on a driving recorder of this application;
- FIG. 4 is a schematic diagram of functional modules of an embodiment of a proof device based on a driving recorder of the present application.
- FIG. 1 is a driving recorder of a hardware operating environment involved in the solution of the embodiment of the present application (the driving recorder may be composed of a separate proof device based on the driving recorder, or may be composed of other devices and driving based
- the combination of the recorder's proof device is a schematic diagram of the structure.
- the driving recorder may include: a processor 1001, such as a central processing unit Central Processing Unit, CPU), network interface 1004, user interface 1003, memory 1005, and communication bus 1002.
- the communication bus 1002 is used to implement connection and communication between these components.
- the user interface 1003 may include a display, an input unit such as a keyboard, and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
- the network interface 1004 may optionally include a standard wired interface and a wireless interface.
- the memory 1005 may be a high-speed RAM memory or a non-volatile memory. memory), such as disk storage.
- the memory 1005 may optionally be a storage device independent of the foregoing processor 1001.
- the driving recorder may further include a camera, RF (Radio Frequency (radio frequency) circuits, sensors, audio circuits, WiFi modules; input units, display screens, touch screens; network interfaces are optional except wireless interfaces except WiFi, Bluetooth, probes, etc.
- sensors such as light sensors, motion sensors, and other sensors.
- the computer software product is stored in a storage medium (storage medium: also called computer storage medium, computer medium, readable medium, readable storage medium, computer-readable storage medium, or directly called medium, etc., which can be
- the non-volatile readable storage medium (such as RAM, magnetic disk, and optical disk) includes a plurality of instructions for causing a driving recorder to execute the methods described in the embodiments of the present application
- the memory 1005 is a computer storage medium. It may include an operating system, a network communication module, a user interface module, and computer-readable instructions.
- the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server;
- the user interface 1003 is mainly used to connect to the client (user terminal, terminal), and perform data communication with the client.
- the processor 1001 may be used to call the computer-readable instructions stored in the memory 1005 and execute the steps in the proof method based on the driving recorder provided in the following embodiments of the present application.
- the driving recorder-based proof method includes:
- Step S10 Receive a vehicle accident proof request, and obtain accident information included in the vehicle accident proof request.
- the vehicle is provided with a driving recorder.
- the driving recorder is provided with a communication module, a front camera and / or a rear camera.
- the communication module of the driving recorder communicates with the vehicle owner terminal and a server.
- the server may be a traffic management platform. , An insurance platform, a vehicle violation reporting platform and / or a public security system platform.
- the server may be a device corresponding to a vehicle accident handling system of an insurance company or a device corresponding to a traffic police accident handling system.
- the accident information refers to the accident-related information entered by the user, such as the time of the accident, the address of the accident, the license plate number of the accident vehicle, etc.
- the terminal receives the accident information input by the user and triggers the vehicle accident proof request based on the accident information.
- the terminal sends the vehicle accident proof request to the driving recorder, the driving recorder receives the vehicle accident proof request, and obtains the vehicle accident proof request included in the vehicle accident proof request.
- Accident information to determine video information that needs to be obtained based on the accident information.
- Step S20 Obtain an accident occurrence time in the accident information, use video information corresponding to the accident occurrence time as accident video information, and input the accident video information into a preset recognition model to obtain a recognition result.
- the driving recorder acquires an accident occurrence time in the accident information, and acquires video information captured by a camera as the accident video information according to the accident occurrence time, that is, the driving recorder acquires the accident occurrence time in the accident information, and obtains the accident time information.
- the video information of a preset time period of the time interval of the accident is described, and the video information is used as the video information of the accident, wherein the preset time period is a preset time interval, for example, set to 1 minute, for example, an accident input by a user
- the incident was at 12:01 on June 7, 2018, and the driving recorder obtained video information on June 7, 2018, from 12:00 to 12:02, as the accident video information.
- the driving recorder After obtaining the accident video information, the driving recorder processes the accident video information to determine whether the accident video information can be used as proof information, where the proof information refers to information used for accident alarm. That is, the driving recorder inputs the acquired accident video information into a preset recognition model.
- the preset recognition model refers to a preset video recognition model, and the preset recognition model may be a vehicle recognition mode trained with human assistance.
- the preset recognition model divides the accident video information into frames of video images.
- the preset recognition model determines the vehicle and vehicle driving external environment information of each frame of video images, and the driving recorder according to each frame of video images of the vehicle and vehicle driving external environment information Generate the recognition result corresponding to the accident video information.
- Step a1 the driving recorder inputs the accident video information to each recognition sub-model of a preset recognition model, wherein the recognition sub-model includes: a vehicle recognition sub-model and a license plate recognition sub-model;
- Step a2 The terminal uses the vehicle identification sub-model to process the accident video information to obtain a target vehicle included in the accident video information.
- Step a3 The terminal processes the target vehicle by using the license plate recognition sub-model to obtain a license plate number of the target vehicle; and uses the target vehicle and the license plate number as a recognition result of the accident video information.
- the driving recorder inputs the accident video information to each recognition sub-model of the preset recognition model, wherein the recognition sub-model includes: a vehicle recognition sub-model and a license plate recognition sub-model; that is, it is necessary to analyze the accident video information during analysis.
- the accident video information is analyzed and identified in multiple dimensions, for example, identifying the vehicle and its license plate number included in the accident video information, identifying the scene information of the accident, etc. Therefore, the preset recognition model includes different recognition submodels. To identify the accident video information; specifically:
- the terminal inputs the accident video information into a vehicle recognition sub-model of a preset recognition model.
- the vehicle recognition sub-model refers to a pre-trained vehicle recognition model.
- the vehicle recognition sub-model first splits the accident video information into frames of video images. , And then identify the vehicles and other features included in the video image. Among them, the vehicle recognition submodel recognizes the vehicles and other scenes contained in the video image, which can be implemented by a vehicle classifier.
- the vehicle classifier retains the result of the same object appearing in consecutive multiple frames of images and is continuously detected as a vehicle by the vehicle classifier, and removes the result of the same object appearing in successive multiple frames of images and being continuously judged as a non-vehicle by the vehicle classifier.
- the terminal uses the vehicles that appear multiple times and the vehicles related to the accident as the target vehicle.
- the vehicle classifier may optionally include an Adaboost iterative algorithm classifier, SVM (Support Vector Machine) classifier.
- the driving recorder uses the license plate recognition submodel to perform the following operations: 1. determine the location of the vehicle license plate, locate the license plate, locate the license plate location in the vehicle picture; Segmentation to separate the characters in the license plate; 3. Recognize the characters of the vehicle license plate, identify the segmented characters, and finally form the license plate number.
- the specific implementation principle of the license plate recognition sub-model is not described in this embodiment.
- the terminal uses the target vehicle and its license plate number as a recognition result of the accident video information.
- the driving recorder can also use other models to correspondingly recognize that the accident video information contains accident scene information; that is, the accident video information obtained from the driving recorder contains multiple frames of video. Images, some video images are images before the accident, some video images are images during the accident, and some video images are images after the vehicle accident. The driving recorder needs to obtain video images that can represent the accident scene.
- the user does not need to manually take a photo of the accident scene, and log in to the accident alarm platform to upload the captured accident scene photo.
- the user only needs to trigger a vehicle accident proof request on the mobile phone, and the driving recorder associated with the mobile phone can automatically obtain the accident Video information to prove the accident.
- Step S30 According to the recognition result, obtain the target video image of each frame in the accident video information that meets a preset proof rule.
- the driving recorder obtains target video images of each frame in the accident video information that meets a preset proof rule according to the recognition result. That is, there may be video images not related to the accident in the video information initially acquired by the driving recorder.
- the driving recorder excludes the video images not related to the accident according to the recognition result, and obtains the video images related to the accident for processing, and obtains a video that meets the preset rules of proof.
- the preset proof rule refers to the preset proof rule in the driving recorder. For example, the number of proof images does not exceed 6 and the size does not exceed 100kb. Specifically, it includes:
- Step b1 Acquire the target vehicle in the recognition result, and obtain video frames of the target vehicle in the accident video information to form an initial video image set;
- Step b2 Process each frame of video images in the initial video image set according to a preset proof rule to obtain a target video image of each frame that meets the preset proof rule.
- the driving recorder first determines the target vehicle in the recognition result, and obtains each frame video image of the target vehicle in the accident video information to form an initial video image set, that is, the driving recorder will capture the After the accident-free video information is deleted, the driving recorder determines the initial video image set, and processes each frame of video images in the initial video image set according to the preset proof rules to obtain the target video images of each frame that meet the preset proof rules. The driving recorder adjusts each frame of video images in the initial video image set according to the requirements of a preset proof rule to obtain the target video image for the proof of the accident.
- the driving recorder processes the accident video information according to the recognition result to obtain a video image that satisfies a preset proof rule without manual processing by the user, making the accident video processing more intelligent and convenient.
- step S40 the target video image in each frame is used as proof information and sent to a traffic management platform for reporting.
- the driving recorder uses the target video image in each frame as proof information and sends it to the traffic management platform for reporting. It should be added that due to the restrictions of the installation position of the driving recorder, the video image taken by the driving recorder may be Incomplete, the driving recorder needs to further determine whether to add some proof information, specifically:
- Step c1 the driving recorder inputs the target video image of each frame into a preset scene recognition model to obtain scene information of the target video image of each frame; and determines whether the target video image of each frame can be determined based on the scene information. Determine the accident situation;
- Step c2 if the target video image of each frame can determine the accident situation, send the target video image of each frame as proof information and send it to the traffic management platform for reporting;
- Step c3 if the target video image of each frame cannot determine the accident situation, send prompt information to the preset terminal to prompt the preset terminal to upload the accident photo corresponding to the user; receive the accident photo sent by the preset terminal, and The accident photos and the target video images in each frame are used as proof information and sent to the traffic management platform for reporting.
- the driving recorder inputs the target video image of each frame into a preset scene recognition model to obtain scene information of the target video image of each frame; wherein the situation recognition model refers to a preset set for determining the target video image.
- the driving recorder inputs the target video image of each frame into a preset scene recognition model, and the preset scene recognition model processes the target video image to obtain scene information, where the scene information includes: whether it is a rear-end collision , Whether the information is not given as required.
- the driving recorder determines whether the accident situation can be determined based on the target video image of each frame according to the scene information; if the description information corresponding to the accident situation included in the scene information, the accident situation can be determined based on the target video image of each frame , The target video image in each frame is used as proof information and sent to the traffic management platform for reporting; if the description information corresponding to the accident situation is not included in the scene information, the accident situation cannot be determined according to the target video image in each frame, Then, a prompt message is sent to a preset terminal, where the preset terminal refers to a user terminal connected to a preset driving recorder to prompt the preset terminal to upload a photo of the accident corresponding to the user; the user takes a photo according to the prompt information, and The accident photos are sent to the driving recorder, and the driving recorder receives the accident photos sent by the preset terminal, uses the accident photos and the target video images of each frame as proof information, and sends them to the traffic management platform for reporting.
- the driving recorder can automatically extract the video information of the accident and send a report to the traffic management platform to make reporting of the vehicle accident more convenient.
- the video information captured by the driving recorder is used as the accident proof information, the authenticity of the accident proof is high.
- the proof method based on the driving recorder includes:
- Step S50 Obtain the license plate number in the recognition result, and compare the license plate number with each illegal license plate number in a preset license plate set.
- the driving recorder obtains the license plate number in the recognition result, and the driving recorder compares the license plate number with each illegal license plate number in a preset license plate set, where the preset license plate set refers to a preset illegal vehicle license plate set,
- the preset license plate set may be the license plate number of the modified vehicle provided by the vehicle management or the accident vehicle information provided by the public security bureau.
- Step S60 if the license plate number matches an illegal license plate number in the preset number set, determine that the target vehicle corresponding to the license plate number is an illegal vehicle, and send a reminder message to the illegal vehicle management platform.
- the driving recorder determines that the target vehicle corresponding to the license plate number is illegal Vehicles, and send reminders to illegal vehicle management platforms.
- illegal vehicles can be identified to effectively perform vehicle management, avoid illegal vehicles from affecting driving safety, and improve road safety.
- this embodiment is a specific embodiment for providing evidence in violation of regulations.
- the proof method based on the driving recorder includes:
- Step S70 Receive a request for proof of violation of a vehicle, collect environmental video information of the environment in which the vehicle is located, and determine a standard road condition rule corresponding to each frame of video images in the environmental video information.
- the driving recorder receives a request for proof of violation of a vehicle.
- a user triggers a request for proof of violation of a vehicle on a mobile phone, or the driving recorder previously Set the conditions for proof of violations of vehicles.
- the driving recorder When the conditions for preset violations of vehicles are met, a request for proof of violations will be triggered automatically.
- the driving recorder receives a request for proof of violations, the driving recorder will automatically collect environmental video information of the vehicle's environment. The driving recorder determines each frame video image of the environmental video information, and determines the standard road condition rule corresponding to each frame video image.
- the video recorder uses the set image analysis module to analyze each frame of the captured video information, and the image analysis module analyzes each video image in the collected video information.
- the driving recorder obtains the corresponding standard road condition rules according to the road scene, wherein the standard road condition rules are preset road traffic rules, including: no traffic rules, no speeding rules, etc.
- the driving recorder determines the specific implementation of the standard road condition rule according to each frame of video images in the environmental video information. For example: 1. The driving recorder determines whether the current traffic light in the video image is in a red light state. The traffic light is in the red light state, and the standard road condition rule corresponding to the video image is no traffic; 2. The driving recorder determines whether the current road in the video image is set with a speed limit sign. If the current road in the video image is set with a speed limit Mark, the standard road condition rules corresponding to video images are prohibition of speeding and so on.
- Step S80 Obtain a target object of the video image in each frame of the environmental video information, and determine position information of the target object.
- the driving recorder After the driving recorder determines the corresponding standard road condition rules of the video image, the driving recorder obtains the target object of the video image in each frame of the environmental video information. After the driving recorder determines the target object, further determines the position information position of the target object Information, such as whether the motor vehicle is on a non-motorized lane, whether a pedestrian is on a sidewalk, etc.
- the target object refers to the driving and vehicles in the video image of each frame.
- the driving recorder determines the target object of the video image in each frame of the environmental video information.
- a pedestrian classifier and / or a vehicle pedestrian classifier can be used for pedestrian classification. And / or a vehicle pedestrian classifier, reference may be made to the first embodiment of the present application, and details are not described in this embodiment.
- Step S90 Determine whether the target object complies with a preset standard road condition rule according to the target object and the position information thereof.
- the driving recorder judges whether the target object complies with the standard road condition rule according to the target object and the position information, That is, when the traffic recorder obtains the current road condition information and the position information of the target object, it will determine whether the position information of the target object matches the current road condition information of the vehicle.
- the current road condition information is that a red light prohibits the vehicle from moving forward, and If the position of a vehicle in the image is above the sidewalk, the position information of the vehicle is considered not to match the current road condition information; when the current road condition information is a street light, and when someone in the image is on the sidewalk, the target object is considered not to match the current road condition information ; When the current road condition information is forbidden to turn, and when a vehicle in the image turns at an intersection, it is determined that the position information of the vehicle does not match the current road condition information. When the driving recorder determines that the position information of the target object does not match the current road condition information, it is determined that the target object violates the regulations.
- step S100 if the target object does not comply with a preset standard road condition rule, it is determined that the target object is in violation, and the violation video image including the violation target object is sent to a vehicle violation reporting platform.
- the driving recorder determines that the target object does not comply with the standard road conditions (vehicle violations occur)
- the driving recorder transmits the video image and related information to the vehicle violation reporting platform (or server) through the communication module, so that the vehicle The violation reporting platform learns the violation information in time.
- Step d1 if the target object does not meet the preset standard road condition rule, determine that the target object violates the rules, and determine whether the type of the target object is a vehicle;
- Step d2 if the type of the target object is a vehicle, use the vehicle as a violation vehicle, identify the license plate number of the violation vehicle, and determine the current geographic location of the violation vehicle based on a driving recorder, and change the current location of the violation vehicle. Geographical location as illegal vehicle location;
- step d3 the position of the offending vehicle, the license plate number, and the offending video image including the offending target vehicle are taken as offending information and sent to the offending reporting platform of the vehicle.
- the driving recorder determines that the target object violates the rules and determines whether the type of the target object is a vehicle.
- the driving recorder changes the The vehicle is an illegal vehicle, and the license plate number of the illegal vehicle is identified, and the current geographic position of the illegal vehicle is determined based on the driving recorder.
- the driving recorder is also provided with a positioning module for the positioning function. The driving recorder can be obtained through the positioning module.
- the current geographic location of the vehicle uses the current geographic location as the location of the violation vehicle, and sends the location of the vehicle violation to the vehicle violation reporting platform so that the vehicle violation reporting platform understands the vehicle violation information.
- the driving recorder collects an image of a surrounding environment, determines current road condition information according to the image, and positions information of a target object in the image. When the location information does not match the current road condition information, the target object is determined to be in violation; because the driving recorder can automatically identify the target object's violations, the user does not need to manually retrieve the image in the driving recorder to determine the target object's violation, which improves the user Experience.
- an embodiment of the present application further provides a proof device based on a driving recorder.
- the proof device based on a driving recorder includes:
- the receiving acquisition module 10 is configured to receive a vehicle accident proof request, and obtain the accident information contained in the vehicle accident proof request;
- the acquisition and identification module 20 is configured to acquire an accident occurrence time in the accident information, use video information corresponding to the accident occurrence time as accident video information, and input the accident video information into a preset recognition model to obtain recognition. result;
- An image obtaining module 30, configured to obtain, according to the recognition result, a target video image of each frame in the accident video information that meets a preset proof rule;
- the reporting module 40 is configured to send the target video image in each frame as proof information and send it to a traffic management platform for reporting.
- the acquisition and identification module 20 includes:
- An obtaining unit configured to obtain an accident occurrence time in the accident information, and acquire video information at a preset time interval from the accident occurrence time;
- An input unit is configured to use the video information as accident video information, and input the accident video information to each recognition sub-model of a preset recognition model, where the recognition sub-model includes: a vehicle recognition sub-model and a license plate Identifying sub-models;
- a license plate recognition unit configured to process the accident video information by using the vehicle identification sub-model to obtain a target vehicle included in the accident video information
- a scene recognition unit configured to process the target vehicle by using the license plate recognition sub-model to obtain a license plate number of the target vehicle
- a result determining unit configured to use the target vehicle and its license plate number as a recognition result of the accident video information.
- the image acquisition module 30 includes:
- An obtaining unit configured to obtain the target vehicle in the recognition result, obtain each frame video image of the target vehicle in the accident video information, and form an initial video image set;
- the processing unit is configured to process each frame of video images in the initial video image set according to a preset proof rule to obtain a target video image of each frame that meets the preset proof rule.
- the proof device based on the driving recorder further includes:
- a number comparison module configured to obtain the license plate number in the recognition result, and compare the license plate number with each illegal license plate number in a preset license plate set;
- the comparison sending module is configured to determine that the target vehicle corresponding to the license plate number is an illegal vehicle if the license plate number matches an illegal license plate number in the preset number set, and send prompt information to the illegal vehicle management platform.
- the report report module 40 includes:
- a situation recognition unit configured to input the target video image of each frame into a preset scene recognition model to obtain scene information of the target video image of each frame;
- a situation determination unit configured to determine whether the target video image of each frame can determine an accident situation according to the scene information
- a first sending unit configured to send the target video image of each frame as proof information and report it to a traffic management platform if the target video image of each frame can determine an accident situation
- Sending a prompting unit configured to send prompting information to a preset terminal if the target video image of each frame cannot determine an accident situation, to prompt the preset terminal to upload a photo of the accident corresponding to the user;
- a second sending unit is configured to receive an accident photo sent by a preset terminal, use the accident photo and the target video image of each frame as proof information, and send the information to a traffic management platform for reporting.
- the proof device based on the driving recorder further includes:
- the evidence violation module is configured to receive a request for evidence violation of a vehicle, collect environmental video information of the environment in which the vehicle is located, and determine a standard road condition rule corresponding to each frame of video images in the environmental video information;
- An acquisition positioning module configured to acquire a target object of the video image in each frame of the environmental video information, and determine position information of the target object
- An illegal image acquisition module configured to determine whether the target object meets a preset standard road condition rule according to the target object and the position information of the target object;
- a violation report module is configured to determine that the target object is in violation if the target object does not meet the preset standard road condition rule, and send the violation video image including the violation target object to the vehicle violation reporting platform.
- the violation reporting module includes:
- a type judging unit configured to determine that the target object violates the rules if the target object does not meet a preset standard road condition rule, and determine whether the type of the target object is a vehicle;
- a location acquiring unit configured to: if the type of the target object is a vehicle, use the vehicle as a violation vehicle, identify the license plate number of the violation vehicle, and determine the current geographic location of the violation vehicle based on a driving recorder, The current geographic position is used as the illegal vehicle position;
- a reporting unit is used to use the location of the offending vehicle, the license plate number, and the offending video image of the offending target vehicle as offending information, and send it to the offending reporting platform of the vehicle.
- an embodiment of the present application also provides a computer storage medium.
- Computer-readable instructions are stored on the computer storage medium, and when the computer-readable instructions are executed by a processor, the operations in the method for proofing based on a driving recorder provided in the foregoing embodiment are implemented.
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Abstract
一种基于行车记录仪的举证方法，包括以下步骤：接收车辆事故举证请求，获取车辆事故举证请求中包含的事故信息（S10）；获取事故信息中的事故发生时间，将事故发生时间对应的视频信息作为事故视频信息，并将事故视频信息输入至预设识别模型中，得到识别结果（S20）；根据识别结果，获取事故视频信息中符合预设举证规则的各帧目标视频图像（S30）；将各帧目标视频图像作为举证信息并发送至交通管理平台进行报案（S40），有效的提高了举证的便捷性。
Description
本申请要求于2018年08月17日提交中国专利局、申请号为201810951315.2发明名称为“基于行车记录仪的举证方法、装置、设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及车载设备领域，尤其涉及基于行车记录仪的举证方法、装置、设备和存储介质。
背景技术
由于车辆数量不断增加，人们更加重视交通安全，为了避免交通事故的发生，人们采取不同的交通安全措施。
现有的车辆车主通常在车内安装行车记录仪，行车记录仪用于记录驾车行驶过程中周边环境的视频信息，即，行车记录仪的摄像头拍摄车辆周边环境或者车辆内部，在车辆自身出现交通事故或者进行车辆违章举报的时候，车辆车主通过手动调取并查找行车记录仪拍摄的视频信息以进行举证，这样的举证方式需要用户手动操作，使得用户体验较低。如何才可以更加便捷和快速地进行举证成为了目前亟待解决的技术问题。
发明内容
本申请的主要目的在于提供基于行车记录仪的举证方法、装置、设备和存储介质，旨在提高车辆事故举证的便捷性。
为实现上述目的，本申请提供一种基于行车记录仪的举证方法，所述基于行车记录仪的举证方法包括以下步骤：
接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；
获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；
根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；
将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
此外，为实现上述目的，本申请还提供一种基于行车记录仪的举证装置，其特征在于，所述基于行车记录仪的举证装置包括：
接收获取模块，用于接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；
获取识别模块，用于获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；
图像获取模块，用于根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；
发送报案模块，用于将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
此外，为实现上述目的，本申请还提供一种基于行车记录仪的举证设备；
所述基于行车记录仪的举证设备包括：摄像头、存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机可读指令，其中：
所述摄像头，用于拍摄获取视频信息；
所述计算机可读指令被所述处理器执行时实现如上所述的基于行车记录仪的举证方法的步骤。
此外，为实现上述目的，本申请还提供计算机存储介质；
所述计算机存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现如上述的基于行车记录仪的举证方法的步骤。
本申请实施例提出基于行车记录仪的举证方法、装置、设备和存储介质，通过接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案；本申请中在发生交通事故时，不需要人工查看行车记录仪拍摄的视频信息，行车记录仪可以自动进行事故视频信息的提取，并发送的交通管理平台进行报案，使得车辆事故的举证更加便捷，此外，由于将行车记录仪拍摄的视频信息作为事故举证信息，使得事故举证的真实性较高。
附图说明
图1是本申请实施例方案涉及的硬件运行环境的装置结构示意图；
图2为本申请基于行车记录仪的举证方法第一实施例的流程示意图；
图3为本申请基于行车记录仪的举证方法第三实施例的流程示意图；
图4为本申请基于行车记录仪的举证装置一实施例的功能模块示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
如图1所示，图1是本申请实施例方案涉及的硬件运行环境的行车记录仪（行车记录仪可以是由单独的基于行车记录仪的举证装置构成，也可以是由其他装置与基于行车记录仪的举证装置组合形成）结构示意图。
如图1所示，该行车记录仪可以包括：处理器1001，例如中央处理器Central Processing
Unit，CPU），网络接口1004，用户接口1003，存储器1005，通信总线1002。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏（Display）、输入单元比如键盘（Keyboard），可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选地可以包括标准的有线接口、无线接口。存储器1005可以是高速RAM存储器，也可以是稳定的存储器（non-volatile
memory），例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
可选地，行车记录仪还可以包括摄像头、RF（Radio
Frequency，射频）电路，传感器、音频电路、WiFi模块；输入单元，比显示屏，触摸屏；网络接口可选除无线接口中除WiFi外，蓝牙、探针等等。其中，传感器比如光传感器、运动传感器以及其他传感器。本领域技术人员可以理解，图1中示出的行车记录仪结构并不构成对行车记录仪的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图1所示，该计算机软件产品存储在一个存储介质（存储介质：又叫计算机存储介质、计算机介质、可读介质、可读存储介质、计算机可读存储介质或者直接叫介质等，可以为非易失性可读存储介质，如RAM、磁碟、光盘）中，包括若干指令用以使得一台行车记录仪执行本申请各个实施例所述的方法，作为一种计算机存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及计算机可读指令。
在图1所示的行车记录仪中，网络接口1004主要用于连接后台服务器，与后台服务器进行数据通信；用户接口1003主要用于连接客户端（用户端、终端），与客户端进行数据通信；而处理器1001可以用于调用存储器1005中存储的计算机可读指令，并执行本申请以下实施例提供的基于行车记录仪的举证方法中的步骤。
参照图2，在本申请基于行车记录仪的举证方法的第一实施例中，所述基于行车记录仪的举证方法包括：
步骤S10，接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息。
车辆上设置有行车记录仪，行车记录仪上设置有通信模块、前置摄像头和/或后置摄像头，行车记录仪的通信模块与车主终端和服务器进行通信连接，其中，服务器可以是交通管理平台、保险平台、车辆违章举报平台和/或公安系统平台，例如，服务器可以是保险公司车辆事故处理系统对应设备或者交警事故处理系统对应设备。
车辆在道路上出现交通事故时，用户在手机（终端）上输入事故信息，事故信息是指用户输入的事故相关信息，例如，事故发生时间信息、事故发生地址信息、事故车辆的车牌号码等等，终端接收用户输入的事故信息并基于事故信息触发车辆事故举证请求，终端将车辆事故举证请求发送至行车记录仪，行车记录仪接收车辆事故举证请求，并获取所述车辆事故举证请求中包含的事故信息，以根据事故信息确定需要获取的视频信息。
步骤S20，获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果。
行车记录仪获取事故信息中的事故发生时间，并根据所述事故发生时间获取摄像头拍摄的视频信息作为事故视频信息，即，行车记录仪获取所述事故信息中的事故发生时间，并获取与所述事故发生时间间隔预设时间段的视频信息，并将所述视频信息作为事故视频信息，其中，预设时间段为预先设置的时间间隔，例如，设置为1分钟，例如，用户输入的事故事件为2018年6月7日12：01分，则行车记录仪获取2018年6月7日12：00-12：02视频信息作为事故视频信息。
在获取到事故视频信息之后，行车记录仪对事故视频信息进行处理，确定事故视频信息是否可以作为举证信息，其中，举证信息是指用于事故报警的信息。即，行车记录仪将获取到的事故视频信息输入至预设识别模型，预设识别模型是指预先设置的视频识别模型，预设识别模型可以是在人工辅助下训练得到的车辆识别模式。预设识别模型将事故视频信息拆分为各帧视频图像，预设识别模型确定各帧视频图像的车辆和车辆驾驶外界环境信息，行车记录仪根据各帧视频图像的车辆和车辆驾驶外界环境信息生成事故视频信息对应的识别结果。
其中，将事故视频信息输入至预设识别模型得到识别结果的具体实现步骤为：
步骤a1，行车记录仪将所述事故视频信息分别输入至预设识别模型的各识别子模型，其中，所述识别子模型包括：车辆识别子模型和车牌识别子模型；
步骤a2，终端利用所述车辆识别子模型对所述事故视频信息进行处理，得到所述事故视频信息中包含的目标车辆；
步骤a3，终端利用所述车牌识别子模型对所述目标车辆进行处理，得到所述目标车辆的车牌号码；将所述目标车辆及其所述车牌号码作为所述事故视频信息的识别结果。
即，行车记录仪将事故视频信息输入至预设识别模型的各识别子模型，其中，所述识别子模型包括：车辆识别子模型和车牌识别子模型；即，在事故视频信息分析时需要对事故视频信息进行多维度的分析识别，例如，识别事故视频信息中包含的车辆及其车辆的车牌号码，识别事故的发生场景信息等等，因此，预设识别模型中包含有不同的识别子模型，对事故视频信息进行识别；具体地：
终端将事故视频信息输入至预设识别模型的车辆识别子模型中，其中，车辆识别子模型是指预先训练的对车辆识别模型，车辆识别子模型首先将事故视频信息拆分为各帧视频图像，然后确定视频图像中包含的车辆和其他景物。其中，车辆识别子模型识别视频图像中包含的车辆和其他景物可以采用车辆分类器实现。车辆分类器保留连续多帧图像中均出现的同一目标且被车辆分类器连续检测为车辆的结果，去除连续多帧图像中均出现的同一目标且被车辆分类器连续判断为非车辆的结果。终端将多次出现的车辆且与事故相关的车辆作为目标车辆，所述车辆分类器可选包括Adaboost迭代算法分类器、
SVM(Support Vector Machine，支持向量机)分类器。
在车辆分类完成后进一步地识别目标车辆的车辆号码，行车记录仪利用车牌识别子模型进行如下操作：1、确定车辆车牌的位置牌照定位，定位车辆图片中的牌照位置；2、将车牌照字符分割，把牌照中的字符分割出来；3、将车辆车牌照字符识别，把分割好的字符进行识别，最终组成牌照号码。其中，车牌识别子模型的具体实现原理本实施例中不作赘述。终端将目标车辆及其所述车牌号码作为所述事故视频信息的识别结果。
可选地，在车辆车牌识别完成之后，行车记录仪利用还可以利用其他模型对应识别出事故视频信息中包含事故场景信息；即，行车记录仪中获取的到事故视频信息中包含有多帧视频图像，有的视频图像是事故发生前的图像，有的视频图像是事故发生中的图像，有的视频图像是车辆事故后的图像，行车记录仪需要获取可以代表事故场景的视频图像。
在本实施例中用户不需要手动拍摄事故场景照片，并登陆事故报警平台上传拍摄的事故场景照片，只需要在手机上触发车辆事故举证请求，与手机关联的行车记录仪就可以自动的获取事故视频信息，以进行事故举证。
步骤S30，根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像。
行车记录仪根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像。即，行车记录仪初始获取的视频信息中可能存在与事故无关的视频图像，行车记录仪根据识别结果排除事故无关的视频图像，并获取事故相关的视频图像进行处理，得到符合预设举证规则的目标视频图像，其中，预设举证规则是指行车记录仪中预先设置的举证规则，例如，举证图像的数量不超过6张，大小不超过100kb；具体地，包括：
步骤b1，获取所述识别结果中的所述目标车辆，获取所述事故视频信息中包含所述目标车辆的各帧视频图像，组成初始视频图像集合；
步骤b2，将所述初始视频图像集合中各帧视频图像按预设举证规则进行处理，得到符合预设举证规则的各帧目标视频图像。
即，行车记录仪首先确定识别结果中的所述目标车辆，并获取所述事故视频信息中包含所述目标车辆的各帧视频图像，组成初始视频图像集合，即，行车记录仪将拍摄的与事故无关的视频信息进行删除，行车记录仪在确定初始视频图像集合后，对初始视频图像集合中各帧视频图像按预设举证规则进行处理，得到符合预设举证规则的各帧目标视频图像，行车记录仪将初始视频图像集合中各帧视频图像按预设举证规则的要求进行调整，得到用于事故举证的目标视频图像。
本实施例中行车记录仪根据识别结果，对事故视频信息进行处理，以得到满足预设举证规则的视频图像，不需要用户手动处理，使得事故视频处理更加智能，便捷。
步骤S40，将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
行车记录仪将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案，需要补充说明的是：由于受到行车记录仪安装的位置等条件的限制，行车记录仪拍摄的视频画面可能不完整，则行车记录仪需要进一步确定是否增加部分举证信息，具体地：
步骤c1，行车记录仪将各帧所述目标视频图像输入至预设场景识别模型，得到各帧所述目标视频图像的场景信息；根据所述场景信息，判断各帧所述目标视频图像能否确定事故情形；
步骤c2，若各帧所述目标视频图像能确定事故情形，则将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案；
步骤c3，若各帧所述目标视频图像不能确定事故情形，则发送提示信息至预设终端，以提示所述预设终端对应用户上传事故照片；接收预设终端发送的事故照片，将所述事故照片和各帧所述目标视频图像作为举证信息，并发送至交通管理平台进行报案。
即，行车记录仪将各帧所述目标视频图像输入至预设场景识别模型，得到各帧所述目标视频图像的场景信息；其中，设情形认定模型是指预先设置的用于确定目标视频图像中事故情形的模型，行车记录仪将各帧所述目标视频图像输入至预设场景识别模型，预设场景识别模型将目标视频图像进行处理得到场景信息，其中，场景信息中包括：是否为追尾，是否为未按规定让行信息。
行车记录仪根据所述场景信息，判断根据各帧所述目标视频图像能否确定事故情形；若场景信息中包含的事故情形对应的描述信息，则根据各帧所述目标视频图像能确定事故情形，则将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案；若场景信息中不包含的事故情形对应的描述信息，则根据各帧所述目标视频图像不能确定事故情形，则发送提示信息至预设终端，其中，预设终端是指预设的行车记录仪连接的用户终端，以提示所述预设终端对应用户上传事故照片；用户根据提示信息进行拍照，并将拍摄的事故照片发送至行车记录仪，行车记录仪接收预设终端发送的事故照片，将所述事故照片和各帧所述目标视频图像作为举证信息，并发送至交通管理平台进行报案。
本申请中在发生交通事故时，不需要人工查看行车记录仪拍摄的视频信息，行车记录仪可以自动进行事故视频信息的提取，并发送的交通管理平台进行报案，使得车辆事故的举证更加便捷，此外，由于将行车记录仪拍摄的视频信息作为事故举证信息，使得事故举证的真实性较高。
进一步的，在本申请基于行车记录仪的举证方法的第一实施例的基础上提出了本申请基于行车记录仪的举证方法的第二实施例，本实施例是围绕识别结果中的车牌号码进行的非法车辆识别。所述基于行车记录仪的举证方法包括：
步骤S50，获取所述识别结果中的所述车牌号码，将所述车牌号码与预设车牌集合中的各非法车牌号码进行比对。
行车记录仪获取识别结果中的车牌号码，行车记录仪将所述车牌号码与预设车牌集合中的各非法车牌号码进行比对，其中，预设车牌集合是指预先设置的非法车辆车牌集合，预设车牌集合可以是由车管所提供的改装车辆的车牌号码或者由公安局提供的肇事车辆信息。
步骤S60，若所述车牌号码与所述预设号码集合中的非法车牌号码匹配，则判定所述车牌号码对应的目标车辆为非法车辆，并发送提示信息至非法车辆管理平台。
若所述车牌号码与所述预设号码集合中的非法车牌号码匹配，即，所述预设号码集合中的存在所述车牌号码，则行车记录仪判定所述车牌号码对应的目标车辆为非法车辆，并发送提示信息至非法车辆管理平台。
本实施例中可以对非法车辆进行识别，以有效地进行车辆管理，避免非法车辆影响行驶安全，提高道路安全性。
进一步地，参照图3，本申请基于行车记录仪的举证方法的第三实施例中，本实施例是违章举证的具体实施例。
所述基于行车记录仪的举证方法包括：
步骤S70，接收车辆违章举证请求，采集车辆所处环境的环境视频信息，确定所述环境视频信息中的各帧视频图像对应的标准路况规则。
行车记录仪接收到车辆违章举证请求，其中，触发车辆违章举证请求的方式有多种，本实施例中不作具体限定，例如，用户在手机上触发车辆违章举证请求，或者是行车记录仪中预先设置车辆违章举证条件，在符合预先设置车辆违章举证条件的条件时，自动触发车辆违章举证请求，行车记录仪接收到车辆违章举证请求时，行车记录仪自动采集车辆所处环境的环境视频信息，行车记录仪确定环境视频信息的各帧视频图像，并确定各帧视频图像对应的标准路况规则。
即，在行车记录仪采集视频图像后，行车记录仪利用设置的图像分析模块，对采集到的视频信息中的各帧图像进行分析，图像分析模块会对采集的视频信息中各个视频图像进行分析，确定视频图像对应的道路场景，行车记录仪根据道路场景获取对应的标准路况规则，其中，标准路况规则是预先设置的道路交通规则，包括：禁止通行规则、禁止超速规则等。
行车记录仪根据所述环境视频信息中的各帧视频图像确定标准路况规则的具体实现方式；例如：1、行车记录仪确定视频图像中当前交通指示灯是否处于红灯状态，若视频图像中当前交通指示灯处于红灯状态，则视频图像对应的标准路况规则为禁止通行；2、行车记录仪确定视频图像中当前道路是上是否设置限速标识，若视频图像中当前道路是上设置限速标识，则视频图像对应的标准路况规则为禁止超速等。
步骤S80，获取环境视频信息中各帧所述视频图像的目标对象，并确定目标对象的位置信息。
行车记录仪确定视频图像的对应的标准路况规则后，行车记录仪获取环境视频信息中各帧所述视频图像的目标对象，行车记录仪确定目标对象之后，进一步地，确定目标对象的位置信息位置信息，例如，机动车辆是否在非机动车道上，行人是否在人行通道上等等。
其中，目标对象是指各帧所述视频图像中的行车和车辆，行车记录仪确定环境视频信息中各帧所述视频图像的目标对象可以采用行人分类器和/或车辆行人分类器，行人分类器和/或车辆行人分类器可以参照本申请第一实施例，本实施例不作赘述。
步骤S90，根据所述目标对象及其所述位置信息，判断所述目标对象是否符合预设标准路况规则。
行车记录仪根据所述目标对象及其所述位置信息，判断目标对象是否符合标准路况规则，
即，行车记录仪获取到当前路况信息以及目标对象的位置信息时，会判断目标对象的位置信息是否匹配车辆所在的当前路况信息，如，在当前路况信息为红灯禁止车辆前行时，且图像中有车辆的位置处于人行道之上，则认为该车辆的位置信息不匹配当前路况信息；在当前路况信息为路灯时，且图像中有人处于人行道时，则认为该目标对象不匹配当前路况信息；在当前路况信息为禁止转向时，且图像中有车辆在路口转向时，则判定该车辆的位置信息不匹配当前路况信息。在当行车记录仪确定目标对象的位置信息不匹配当前路况信息，则确定该目标对象违章。
步骤S100，若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并将包含所述违章目标对象的所述违章视频图像发送至车辆违章举报平台。
若行车记录仪确定目标对象不符合标准路况规则（出现车辆违章的情况），则行车记录仪将视频图像及其相关信息通过通信模块向车辆违章举报平台（或者又叫服务器）传输，以使车辆违章举报平台及时了解到违章信息。
步骤d1，若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并判断所述目标对象的类型是否为车辆；
步骤d2，若所述目标对象的类型是车辆，则将所述车辆作为违章车辆，识别所述违章车辆的车牌号码，并基于行车记录仪确定所述违章车辆的当前地理位置，将所述当前地理位置作为违章车辆位置；
步骤d3，将违章车辆位置、车牌号码和包含违章目标车辆的违章视频图像作为违章信息，并发送至车辆违章举报平台。
在目标对象不符合预设标准路况规则，则行车记录仪确定所述目标对象违章，并判断所述目标对象的类型是否为车辆，在判断目标对象的类型是车辆时，行车记录仪将所述车辆作为违章车辆，识别所述违章车辆的车牌号码，并基于行车记录仪确定所述违章车辆的当前地理位置，行车记录仪上还设置有定位功能的定位模块，行车记录仪可以通过定位模块获取车辆的当前地理位置，将所述当前地理位置作为违章车辆位置，并将车辆违章位置发送至车辆违章举报平台，以使车辆违章举报平台了解车辆违章信息。
在本实施例中行车记录仪采集所处环境的图像，根据图像确定当前路况信息，并在图像内目标对象的位置信息，
在位置信息不匹配当前路况信息时，确定目标对象违章；因行车记录仪能够自动识别目标对象的违章行为，使得用户不需要手动调取行车记录仪中的图像以确定目标对象违章，提高了用户的体验。
此外，参照图4，本申请实施例还提出一种基于行车记录仪的举证装置，所述基于行车记录仪的举证装置包括：
接收获取模10，用于接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；
获取识别模块20，用于获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；
图像获取模块30，用于根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；
发送报案模块40，用于将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
可选地，所述获取识别模块20，包括：
获取单元，用于获取所述事故信息中的事故发生时间，并获取与所述事故发生时间间隔预设时间段的视频信息；
输入单元，用于将所述视频信息作为事故视频信息，并将所述事故视频信息分别输入至预设识别模型的各识别子模型，其中，所述识别子模型包括：车辆识别子模型和车牌识别子模型；
车牌识别单元，用于利用所述车辆识别子模型对所述事故视频信息进行处理，得到所述事故视频信息中包含的目标车辆；
场景识别单元，用于利用所述车牌识别子模型对所述目标车辆进行处理，得到所述目标车辆的车牌号码；
结果确定单元，用于将所述目标车辆及其所述车牌号码作为所述事故视频信息的识别结果。
可选地，所述图像获取模块30，包括：
获取单元，用于获取所述识别结果中的所述目标车辆，获取所述事故视频信息中包含所述目标车辆的各帧视频图像，组成初始视频图像集合；
处理单元，用于将所述初始视频图像集合中各帧视频图像按预设举证规则进行处理，得到符合预设举证规则的各帧目标视频图像。
可选地，所述的基于行车记录仪的举证装置，还包括：
号码比对模块，用于获取所述识别结果中的所述车牌号码，将所述车牌号码与预设车牌集合中的各非法车牌号码进行比对；
比对发送模块，用于若所述车牌号码与所述预设号码集合中的非法车牌号码匹配，则判定所述车牌号码对应的目标车辆为非法车辆，并发送提示信息至非法车辆管理平台。
可选地，所述发送报案模块40，包括：
情形识别单元，用于将各帧所述目标视频图像输入至预设场景识别模型，得到各帧所述目标视频图像的场景信息；
情形判断单元，用于根据所述场景信息，判断各帧所述目标视频图像能否确定事故情形；
第一发送单元，用于若各帧所述目标视频图像能确定事故情形，则将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案；
发送提示单元，用于若各帧所述目标视频图像不能确定事故情形，则发送提示信息至预设终端，以提示所述预设终端对应用户上传事故照片；
第二发送单元，用于接收预设终端发送的事故照片，将所述事故照片和各帧所述目标视频图像作为举证信息，并发送至交通管理平台进行报案。
可选地，所述的基于行车记录仪的举证装置，还包括：
违章举证模块，用于接收车辆违章举证请求，采集车辆所处环境的环境视频信息，确定所述环境视频信息中的各帧视频图像对应的标准路况规则；
获取定位模块，用于获取环境视频信息中各帧所述视频图像的目标对象，并确定目标对象的位置信息；
违章图像获取模块，用于根据所述目标对象及其所述位置信息，判断所述目标对象是否符合预设标准路况规则；
违章举报模块，用于若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并将包含所述违章目标对象的所述违章视频图像发送至车辆违章举报平台。
可选地，所述违章举报模块，包括：
类型判断单元，用于若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并判断所述目标对象的类型是否为车辆；
位置获取单元，用于若所述目标对象的类型是车辆，则将所述车辆作为违章车辆，识别所述违章车辆的车牌号码，并基于行车记录仪确定所述违章车辆的当前地理位置，将所述当前地理位置作为违章车辆位置；
发送举报单元，用于将违章车辆位置、车牌号码和包含违章目标车辆的违章视频图像作为违章信息，并发送至车辆违章举报平台。
其中，基于行车记录仪的举证装置的各个功能模块实现的步骤可参照本申请基于行车记录仪的举证方法的各个实施例，此处不再赘述。
此外，本申请实施例还提出一种计算机存储介质。
所述计算机存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现上述实施例提供的基于行车记录仪的举证方法中的操作。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内
Claims (20)
- 一种基于行车记录仪的举证方法，其特征在于，所述基于行车记录仪的举证方法包括以下步骤：接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
- 如权利要求1所述的基于行车记录仪的举证方法，其特征在于，所述获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果的步骤，包括：获取所述事故信息中的事故发生时间，并获取与所述事故发生时间间隔预设时间段的视频信息；将所述视频信息作为事故视频信息，并将所述事故视频信息分别输入至预设识别模型的各识别子模型，其中，所述识别子模型包括：车辆识别子模型和车牌识别子模型；利用所述车辆识别子模型对所述事故视频信息进行处理，得到所述事故视频信息中包含的目标车辆；利用所述车牌识别子模型对所述目标车辆进行处理，得到所述目标车辆的车牌号码；将所述目标车辆及其所述车牌号码作为所述事故视频信息的识别结果。
- 如权利要求2所述的基于行车记录仪的举证方法，其特征在于，所述根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像的步骤，包括：获取所述识别结果中的所述目标车辆，获取所述事故视频信息中包含所述目标车辆的各帧视频图像，组成初始视频图像集合；将所述初始视频图像集合中各帧视频图像按预设举证规则进行处理，得到符合预设举证规则的各帧目标视频图像。
- 如权利要求2所述的基于行车记录仪的举证方法，其特征在于，所述根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像的步骤之后，包括：获取所述识别结果中的所述车牌号码，将所述车牌号码与预设车牌集合中的各非法车牌号码进行比对；若所述车牌号码与所述预设号码集合中的非法车牌号码匹配，则判定所述车牌号码对应的目标车辆为非法车辆，并发送提示信息至非法车辆管理平台。
- 如权利要求1所述的基于行车记录仪的举证方法，其特征在于，所述将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案的步骤，包括：将各帧所述目标视频图像输入至预设场景识别模型，得到各帧所述目标视频图像的场景信息；根据所述场景信息，判断各帧所述目标视频图像能否确定事故情形；若各帧所述目标视频图像能确定事故情形，则将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案；若各帧所述目标视频图像不能确定事故情形，则发送提示信息至预设终端，以提示所述预设终端对应用户上传事故照片；接收预设终端发送的事故照片，将所述事故照片和各帧所述目标视频图像作为举证信息，并发送至交通管理平台进行报案。
- 如权利要求1所述的基于行车记录仪的举证方法，其特征在于，所述将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案的步骤之后，包括：接收车辆违章举证请求，采集车辆所处环境的环境视频信息，确定所述环境视频信息中的各帧视频图像对应的标准路况规则；获取环境视频信息中各帧所述视频图像的目标对象，并确定目标对象的位置信息；根据所述目标对象及其所述位置信息，判断所述目标对象是否符合预设标准路况规则；若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并将包含所述违章目标对象的所述违章视频图像发送至车辆违章举报平台。
- 如权利要求6所述的基于行车记录仪的举证方法，其特征在于，所述若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并将包含所述违章目标对象的所述违章视频图像发送至车辆违章举报平台的步骤，包括：若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并判断所述目标对象的类型是否为车辆；若所述目标对象的类型是车辆，则将所述车辆作为违章车辆，识别所述违章车辆的车牌号码，并基于行车记录仪确定所述违章车辆的当前地理位置，将所述当前地理位置作为违章车辆位置；将违章车辆位置、车牌号码和包含违章目标车辆的违章视频图像作为违章信息，并发送至车辆违章举报平台。
- 一种基于行车记录仪的举证装置，其特征在于，所述基于行车记录仪的举证装置包括：接收获取模块，用于接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；获取识别模块，用于获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；图像获取模块，用于根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；发送报案模块，用于将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
- 如权利要求8所述的基于行车记录仪的举证装置，其特征在于，所述获取识别模块，包括：获取单元，用于获取所述事故信息中的事故发生时间，并获取与所述事故发生时间间隔预设时间段的视频信息；输入单元，用于将所述视频信息作为事故视频信息，并将所述事故视频信息分别输入至预设识别模型的各识别子模型，其中，所述识别子模型包括：车辆识别子模型和车牌识别子模型；车牌识别单元，用于利用所述车辆识别子模型对所述事故视频信息进行处理，得到所述事故视频信息中包含的目标车辆；场景识别单元，用于利用所述车牌识别子模型对所述目标车辆进行处理，得到所述目标车辆的车牌号码；结果确定单元，用于将所述目标车辆及其所述车牌号码作为所述事故视频信息的识别结果。
- 如权利要求9所述的基于行车记录仪的举证装置，其特征在于，所述图像获取模块，包括：获取单元，用于获取所述识别结果中的所述目标车辆，获取所述事故视频信息中包含所述目标车辆的各帧视频图像，组成初始视频图像集合；处理单元，用于将所述初始视频图像集合中各帧视频图像按预设举证规则进行处理，得到符合预设举证规则的各帧目标视频图像。
- 如权利要求9所述的基于行车记录仪的举证装置，其特征在于，所述的基于行车记录仪的举证装置，还包括：号码比对模块，用于获取所述识别结果中的所述车牌号码，将所述车牌号码与预设车牌集合中的各非法车牌号码进行比对；比对发送模块，用于若所述车牌号码与所述预设号码集合中的非法车牌号码匹配，则判定所述车牌号码对应的目标车辆为非法车辆，并发送提示信息至非法车辆管理平台。
- 如权利要求8所述的基于行车记录仪的举证装置，其特征在于，所述发送报案模块，包括：情形识别单元，用于将各帧所述目标视频图像输入至预设场景识别模型，得到各帧所述目标视频图像的场景信息；情形判断单元，用于根据所述场景信息，判断各帧所述目标视频图像能否确定事故情形；第一发送单元，用于若各帧所述目标视频图像能确定事故情形，则将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案；发送提示单元，用于若各帧所述目标视频图像不能确定事故情形，则发送提示信息至预设终端，以提示所述预设终端对应用户上传事故照片；第二发送单元，用于接收预设终端发送的事故照片，将所述事故照片和各帧所述目标视频图像作为举证信息，并发送至交通管理平台进行报案。
- 如权利要求8所述的基于行车记录仪的举证装置，其特征在于，所述的基于行车记录仪的举证装置，还包括：违章举证模块，用于接收车辆违章举证请求，采集车辆所处环境的环境视频信息，确定所述环境视频信息中的各帧视频图像对应的标准路况规则；获取定位模块，用于获取环境视频信息中各帧所述视频图像的目标对象，并确定目标对象的位置信息；违章图像获取模块，用于根据所述目标对象及其所述位置信息，判断所述目标对象是否符合预设标准路况规则；违章举报模块，用于若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并将包含所述违章目标对象的所述违章视频图像发送至车辆违章举报平台。
- 如权利要求13所述的基于行车记录仪的举证装置，其特征在于，所述违章举报模块，包括：类型判断单元，用于若所述目标对象不符合预设标准路况规则，则确定所述目标对象违章，并判断所述目标对象的类型是否为车辆；位置获取单元，用于若所述目标对象的类型是车辆，则将所述车辆作为违章车辆，识别所述违章车辆的车牌号码，并基于行车记录仪确定所述违章车辆的当前地理位置，将所述当前地理位置作为违章车辆位置；发送举报单元，用于将违章车辆位置、车牌号码和包含违章目标车辆的违章视频图像作为违章信息，并发送至车辆违章举报平台。
- 一种基于行车记录仪的举证设备，其特征在于，所述基于行车记录仪的举证设备包括：摄像头、存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机可读指令，其中：所述摄像头，用于拍摄获取视频信息；所述计算机可读指令被所述处理器执行时实现以下步骤：接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
- 如权利要求15所述的基于行车记录仪的举证设备，其特征在于，所述计算机可读指令被所述处理器执行：所述获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果的步骤，包括：获取所述事故信息中的事故发生时间，并获取与所述事故发生时间间隔预设时间段的视频信息；将所述视频信息作为事故视频信息，并将所述事故视频信息分别输入至预设识别模型的各识别子模型，其中，所述识别子模型包括：车辆识别子模型和车牌识别子模型；利用所述车辆识别子模型对所述事故视频信息进行处理，得到所述事故视频信息中包含的目标车辆；利用所述车牌识别子模型对所述目标车辆进行处理，得到所述目标车辆的车牌号码；将所述目标车辆及其所述车牌号码作为所述事故视频信息的识别结果。
- 如权利要求16所述的基于行车记录仪的举证设备，其特征在于，所述计算机可读指令被所述处理器执行：所述根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像的步骤，包括：获取所述识别结果中的所述目标车辆，获取所述事故视频信息中包含所述目标车辆的各帧视频图像，组成初始视频图像集合；将所述初始视频图像集合中各帧视频图像按预设举证规则进行处理，得到符合预设举证规则的各帧目标视频图像。
- 如权利要求16所述的基于行车记录仪的举证设备，其特征在于，所述计算机可读指令被所述处理器执行以下步骤：获取所述识别结果中的所述车牌号码，将所述车牌号码与预设车牌集合中的各非法车牌号码进行比对；若所述车牌号码与所述预设号码集合中的非法车牌号码匹配，则判定所述车牌号码对应的目标车辆为非法车辆，并发送提示信息至非法车辆管理平台。
- 如权利要求15所述的基于行车记录仪的举证设备，其特征在于，所述计算机可读指令被所述处理器执行：所述将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案的步骤，包括：将各帧所述目标视频图像输入至预设场景识别模型，得到各帧所述目标视频图像的场景信息；根据所述场景信息，判断各帧所述目标视频图像能否确定事故情形；若各帧所述目标视频图像能确定事故情形，则将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案；若各帧所述目标视频图像不能确定事故情形，则发送提示信息至预设终端，以提示所述预设终端对应用户上传事故照片；接收预设终端发送的事故照片，将所述事故照片和各帧所述目标视频图像作为举证信息，并发送至交通管理平台进行报案。
- 一种计算机存储介质，其特征在于，所述计算机存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现以下的步骤：接收车辆事故举证请求，获取所述车辆事故举证请求中包含的事故信息；获取所述事故信息中的事故发生时间，将所述事故发生时间对应的视频信息作为事故视频信息，并将所述事故视频信息输入至预设识别模型中，得到识别结果；根据所述识别结果，获取所述事故视频信息中符合预设举证规则的各帧目标视频图像；将各帧所述目标视频图像作为举证信息并发送至交通管理平台进行报案。
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| CN114582046A (zh) * | 2020-12-01 | 2022-06-03 | 上海博泰悦臻网络技术服务有限公司 | 视频处理方法、系统、介质及电子设备 |
| CN112926575A (zh) * | 2021-01-22 | 2021-06-08 | 北京嘀嘀无限科技发展有限公司 | 交通事故识别方法、装置、电子设备和介质 |
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