A vehicle accident detection method, apparatus, device, and storage medium

Abstract

The application discloses a vehicle accident detection method, device and equipment and a storage medium. The method comprises the following steps: when the distance between a moving object around a vehicle and the vehicle is less than a preset threshold, acquiring the vehicle video of the time period when the distance between the moving object and the vehicle is within the preset threshold, the time point and the distance when the distance between the moving object and the vehicle is the shortest; detecting the relative position area of the moving object and the vehicle at the target time point; determining the vehicle accident risk level according to the vibration condition of the vehicle and the target distance; acquiring the image of the moving object, performing feature extraction on the image, determining the object type of the moving object, and identifying the object features of the moving object according to the object type; acquiring the current parking site of the vehicle, generating a corresponding vehicle accident report, and sending the vehicle accident report and the target video to a corresponding client. The application can improve the event backtracking efficiency of the vehicle owner when the accident occurs.

Description

Technical Field

[0001] This invention relates to the field of vehicle accident detection technology, and in particular to a vehicle accident detection method, apparatus, equipment, and storage medium. Background Technology

[0002] To monitor minor scrapes, collisions, and thefts after parking, many vehicles are equipped with a Sentinel (accident detection) mode. However, when Sentinel mode is triggered, drivers can only understand the incident by reviewing the recorded video. The videos recorded in Sentinel mode range from tens of seconds to over ten minutes, making it difficult for drivers to quickly understand the time, process, and consequences of the incident. Furthermore, the recorded videos may not clearly show the license plate or facial information of the offending vehicle, making it impossible to trace the accident. Summary of the Invention

[0003] This invention provides a vehicle accident detection method, apparatus, equipment, and storage medium to solve the technical problems of existing vehicle accident detection modes that fail to allow vehicle owners to quickly understand the time, process, and consequences of an incident, and that the recorded videos may not clearly show the license plate and facial information of the vehicle involved, making it impossible to trace the accident.

[0004] To address the aforementioned technical problems, embodiments of the present invention provide a vehicle accident detection method, comprising:

[0005] When a moving object is detected around the vehicle, the distance between the moving object and the vehicle is detected;

[0006] When the distance between the moving object and the vehicle is less than a preset threshold, the vehicle video is obtained during the time period when the distance between the moving object and the vehicle is within the preset threshold. The vehicle video is used as the target video, and the time point when the distance between the moving object and the vehicle is the shortest is used as the target time point. The distance when the distance between the moving object and the vehicle is the shortest is used as the target distance.

[0007] Based on the target time point, the relative position area between the moving object and the vehicle at the target time point is detected, and the relative position area is taken as the target area; wherein, the relative position area includes: front left of the vehicle, front right of the vehicle, left side of the vehicle, right side of the vehicle, rear left of the vehicle, and rear right of the vehicle;

[0008] The vehicle's vibration status is obtained based on preset vehicle vibration sensors, and the vehicle accident risk level is determined based on the vibration status and the target distance.

[0009] An image of the moving object is acquired, features are extracted from the image, the object type of the moving object is determined based on the extracted image features, and the object features of the moving object are identified based on the object type; wherein, the object features include: license plate number or human face;

[0010] The system obtains the vehicle's current parking location, generates a corresponding vehicle accident report based on the parking location, target time point, target area, vehicle accident risk level, object type, and object characteristics, and sends the vehicle accident report and the target video to the corresponding client.

[0011] As a preferred embodiment, the vibration status of the vehicle includes: vibration occurring or no vibration occurring;

[0012] The process of determining the vehicle accident risk level based on the vibration conditions and the target distance includes:

[0013] Based on the vibration conditions and the target distance, when the vehicle vibrates, the vehicle accident risk level is determined to be high risk.

[0014] When the vehicle does not vibrate and the target distance does not exceed a preset first distance threshold, the vehicle accident risk level is determined to be medium risk.

[0015] When the vehicle does not vibrate and the target distance is greater than the first distance threshold but not more than the preset second distance threshold, the vehicle accident risk level is determined to be low risk; wherein the first distance threshold is less than the second distance threshold.

[0016] As a preferred embodiment, the step of extracting features from the image and then determining the object type of the moving object based on the extracted image features includes:

[0017] Extract image features from the image; wherein, the image features include: key pixels, image edges, and image patches;

[0018] The extracted image features are compared with image features in a preset image feature library, and the object type of the moving object is determined based on the comparison results; wherein, the object type includes: motor vehicle, non-motor vehicle or pedestrian.

[0019] As a preferred embodiment, identifying the object features of the moving object based on the object type includes:

[0020] When the object type is a motor vehicle or a non-motor vehicle, the texture, color and shape in the image are extracted, the extracted texture, color and shape are analyzed for features, the license plate area where the vehicle license plate is located is located, and the license plate area is used for character recognition to obtain the license plate number of the moving object. The extracted license plate number is used as the object feature of the moving object.

[0021] When the object type is a pedestrian, the facial information of the moving object is extracted from the image according to a preset face detection algorithm, and the extracted facial information is used as the object feature of the moving object.

[0022] Based on the above embodiments, another embodiment of the present invention provides a vehicle accident detection device, including: a vehicle real-time monitoring module, a vehicle video acquisition module, a vehicle area detection module, a vehicle accident risk level determination module, an object feature recognition module, and a vehicle accident report generation module;

[0023] The vehicle real-time monitoring module is used to detect the distance between the moving object and the vehicle when a moving object is detected around the vehicle.

[0024] The vehicle video acquisition module is used to acquire vehicle video during a time period when the distance between the moving object and the vehicle is within a preset threshold when the distance between the moving object and the vehicle is less than a preset threshold, use the vehicle video as the target video, use the time point when the distance between the moving object and the vehicle is the shortest as the target time point, and use the distance when the distance between the moving object and the vehicle is the shortest as the target distance.

[0025] The vehicle area detection module is used to detect the relative position area between the moving object and the vehicle at the target time point, and to use the relative position area as the target area; wherein, the relative position area includes: front left of the vehicle, front right of the vehicle, left side of the vehicle, right side of the vehicle, rear left of the vehicle, and rear right of the vehicle.

[0026] The vehicle accident risk level determination module is used to obtain the vibration of the vehicle based on a preset vehicle vibration sensor, and to determine the vehicle accident risk level based on the vibration and the target distance.

[0027] The object feature recognition module is used to acquire an image of the moving object, extract features from the image, determine the object type of the moving object based on the extracted image features, and identify the object features of the moving object based on the object type; wherein, the object features include: license plate number or human face;

[0028] The vehicle accident report generation module is used to obtain the current parking location of the vehicle, generate a corresponding vehicle accident report based on the parking location, target time point, target area, vehicle accident risk level, object type and object characteristics, and send the vehicle accident report and the target video to the corresponding client.

[0029] As a preferred embodiment, the vibration status of the vehicle includes: vibration occurring or no vibration occurring;

[0030] The process of determining the vehicle accident risk level based on the vibration conditions and the target distance includes:

[0031] Based on the vibration conditions and the target distance, when the vehicle vibrates, the vehicle accident risk level is determined to be high risk.

[0032] When the vehicle does not vibrate and the target distance does not exceed a preset first distance threshold, the vehicle accident risk level is determined to be medium risk.

[0033] When the vehicle does not vibrate and the target distance is greater than the first distance threshold but not more than the preset second distance threshold, the vehicle accident risk level is determined to be low risk; wherein the first distance threshold is less than the second distance threshold.

[0034] As a preferred embodiment, the step of extracting features from the image and then determining the object type of the moving object based on the extracted image features includes:

[0035] Extract image features from the image; wherein, the image features include: key pixels, image edges, and image patches;

[0036] The extracted image features are compared with image features in a preset image feature library, and the object type of the moving object is determined based on the comparison results; wherein, the object type includes: motor vehicle, non-motor vehicle or pedestrian.

[0037] As a preferred embodiment, identifying the object features of the moving object based on the object type includes:

[0038] When the object type is a motor vehicle or a non-motor vehicle, the texture, color and shape in the image are extracted, the extracted texture, color and shape are analyzed for features, the license plate area where the vehicle license plate is located is located, and the license plate area is used for character recognition to obtain the license plate number of the moving object. The extracted license plate number is used as the object feature of the moving object.

[0039] When the object type is a pedestrian, the facial information of the moving object is extracted from the image according to a preset face detection algorithm, and the extracted facial information is used as the object feature of the moving object.

[0040] Based on the above embodiments, another embodiment of the present invention provides an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the vehicle accident detection method described in the above embodiments of the invention.

[0041] Based on the above embodiments, another embodiment of the present invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the vehicle accident detection method described in the above embodiments of the invention.

[0042] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0043] This invention provides a vehicle accident detection method. When the distance between a moving object around a vehicle and the vehicle is less than a preset threshold, the method acquires vehicle video during a time period when the distance between the moving object and the vehicle is within the preset threshold. This vehicle video is used as the target video, and the time point when the distance between the moving object and the vehicle is shortest is used as the target time point, and the distance when the distance between the moving object and the vehicle is shortest is used as the target distance. Based on the target time point, the method detects the relative position area between the moving object and the vehicle at that time point, and uses this relative position as the target area. The method acquires the vehicle's vibration status using a preset vehicle vibration sensor, and determines the vehicle accident risk level based on the vibration status and the target distance. The method acquires an image of the moving object, extracts features from the image, determines the object type based on the extracted image features, and identifies the object characteristics based on the object type. The method acquires the vehicle's current parking location, and generates a corresponding vehicle accident report based on the parking location, target time point, target area, vehicle accident risk level, object type, and object characteristics. The method then sends the vehicle accident report and the target video to the corresponding client.

[0044] Through this invention, car owners can quickly understand the vehicle's location, time, course of events, and risk level of an accident by using the parking location, target time, target area, and vehicle accident risk level in the vehicle accident report. They can also use the object type and characteristics in the vehicle accident report to hold the perpetrator or vehicle accountable. Furthermore, by viewing the target video, they can view vehicle videos within a preset threshold time period when the distance between the moving object and the vehicle is within the threshold, thus understanding the entire process of the accident and greatly improving the efficiency of event retrieval for car owners when an accident occurs. Attached Figure Description

[0045] Figure 1 This is a schematic flowchart of a vehicle accident detection method provided in an embodiment of the present invention;

[0046] Figure 2 This is a screenshot of the vehicle accident report interface;

[0047] Figure 3 This is a schematic diagram of the structure of a vehicle accident detection device provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0050] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0051] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0052] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0053] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0054] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0055] Example 1

[0056] Please refer to Figure 1 The following is a flowchart illustrating a vehicle accident detection method according to an embodiment of the present invention, comprising the following specific steps:

[0057] S1. When a moving object is detected around the vehicle, the distance between the moving object and the vehicle is detected;

[0058] Specifically, addressing the problem that existing technologies cannot allow car owners to quickly understand the time, process, and consequences of an incident, and that recorded videos may not clearly show the license plate and facial information of the offending vehicle, leading to an inability to trace the accident, this invention proposes a sentinel mode accident report generation method based on image recognition. Image recognition and target tracking technologies are integrated into the domain controller. When sentinel mode (i.e., accident detection mode) is activated, the surround-view camera continues to operate. When the domain controller detects a risk event, the image processing module is activated, tracking and recognizing key information such as license plates and faces. After the risk video is latched, the system generates a vehicle accident report. The report should include: the time and location of the risk event, the event level, the location of damage, images of the suspected offending vehicle, personnel, or object, etc., which are saved along with the video to the vehicle's infotainment system for easy viewing by the car owner. The specific process is as follows:

[0059] (1) After the Sentinel Mode (i.e., accident detection mode) is activated, the sensors (camera, ultrasonic, vibration sensors, etc.) deployed on the vehicle continuously detect the environment around the vehicle and record images around the vehicle in a loop through the camera. When the sensors on the vehicle detect information of a moving object around the vehicle, the distance between the moving object and the vehicle is detected in real time to determine whether the Sentinel Mode latching condition is met. The Sentinel Mode latching condition is: the distance D between the moving object and the vehicle detected by the sensor is less than the preset threshold D1.

[0060] S2. When the distance between the moving object and the vehicle is less than a preset threshold, obtain the vehicle video during the time period when the distance between the moving object and the vehicle is within the preset threshold, use the vehicle video as the target video, use the time point when the distance between the moving object and the vehicle is the shortest as the target time point, and use the distance when the distance between the moving object and the vehicle is the shortest as the target distance.

[0061] (2) When the distance D between the moving object and the vehicle is less than the preset threshold D1, the video of X minutes before and after the time period from when the sentry mode latching condition is met to when the sentry mode latching condition is not met (D>D1) is latched, and the vehicle video is stored as the target video in the corresponding storage module.

[0062] The time point at which the shortest distance between the moving object and the vehicle is detected during the latching period is recorded as the target time point T1, and the shortest distance is recorded as the target distance Dmin.

[0063] S3. Based on the target time point, detect the relative position area between the moving object and the vehicle at the target time point, and take the relative position area as the target area; wherein, the relative position area includes: front left of the vehicle, front right of the vehicle, left side of the vehicle, right side of the vehicle, rear left of the vehicle, and rear right of the vehicle;

[0064] (3) Divide the area around the vehicle into 6 regions, namely the front left, front right, left side, right side, rear left, and rear right. The area where the sensor detects the relative position of the moving object and the vehicle at time T1 is recorded as the target area F.

[0065] S4. Obtain the vehicle's vibration status based on the preset vehicle vibration sensor, and determine the vehicle accident risk level based on the vibration status and the target distance;

[0066] Preferably, the vibration status of the vehicle includes: vibration occurring or no vibration occurring; determining the vehicle accident risk level based on the vibration status and the target distance includes: determining the vehicle accident risk level as high-risk when the vehicle vibrates; determining the vehicle accident risk level as medium-risk when the vehicle does not vibrate and the target distance does not exceed a preset first distance threshold; determining the vehicle accident risk level as low-risk when the vehicle does not vibrate, the target distance is greater than the first distance threshold but does not exceed a preset second distance threshold; wherein the first distance threshold is less than the second distance threshold.

[0067] (4) The system classifies the vehicle accident risk level based on the distance of the approaching object (i.e., the target distance Dmin) and the vehicle vibration data obtained by the vehicle vibration sensor. The vehicle accident risk level is denoted as C:

[0068] ① High-risk event: The vehicle vibration sensor detects vibration in the vehicle;

[0069] ② Medium-risk event: The vehicle vibration sensor does not detect vehicle vibration, and the minimum distance between the sensor-detected moving object and the vehicle, Dmin ≤ D2; where D2 is the preset medium-risk threshold, i.e. the first distance threshold;

[0070] ③Low-risk event: The vehicle vibration sensor does not detect any vibration in the vehicle, and the minimum distance between the sensor-detected object and the vehicle is D2 < Dmin ≤ D1; where D1 is the preset low-risk threshold, i.e. the second distance threshold, and D2 < D1.

[0071] S5. Acquire an image of the moving object, extract features from the image, determine the object type of the moving object based on the extracted image features, and identify the object features of the moving object based on the object type; wherein, the object features include: license plate number or human face;

[0072] Preferably, the step of extracting features from the image and then determining the object type of the moving object based on the extracted image features includes: extracting image features from the image; wherein the image features include: key pixels, image edges, and image patches; comparing the extracted image features with image features in a preset image feature library, and then determining the object type of the moving object based on the comparison result; wherein the object type includes: motor vehicles, non-motor vehicles, or pedestrians.

[0073] Preferably, the step of identifying the object features of the moving object based on the object type includes: when the object type is a motor vehicle or a non-motor vehicle, extracting the texture, color, and shape from the image, performing feature analysis on the extracted texture, color, and shape, locating the license plate area where the vehicle license plate is located, and performing character recognition on the license plate area to obtain the license plate number of the moving object, and using the extracted license plate number as the object feature of the moving object; when the object type is a pedestrian, extracting the facial information of the moving object from the image according to a preset face detection algorithm, and using the extracted facial information as the object feature of the moving object.

[0074] (5) After the sentry mode latching conditions are met, the object type E of the moving object detected by the sensor (camera) is identified, and the key pixels, edges or patches in the image of the moving object captured by the camera are extracted and compared with the image features in the preset image feature library to determine the object type; among them, the object type is divided into: motor vehicle, non-motor vehicle, pedestrian or others.

[0075] The sensor identifies the object features Y of object type E detected by the sensor: ① When the detected object is a motor vehicle or a non-motor vehicle, feature analysis is performed using texture, color, and shape to locate the license plate area. Then, optical character recognition technology is used to identify the segmented characters and convert them into numbers and letters to extract the vehicle's license plate number; ② When the detected object is a pedestrian, a face detection algorithm (such as a cascaded classifier based on Haar features or a neural network-based method) is used to separate the face from the background; other objects are not processed.

[0076] S6. Obtain the vehicle's current parking location, generate a corresponding vehicle accident report based on the parking location, target time point, target area, vehicle accident risk level, object type, and object characteristics, and send the vehicle accident report and the target video to the corresponding client.

[0077] (6) Obtain the GNSS positioning information of the vehicle when it is parked, and record the parking location as L.

[0078] Please refer to Figure 2The interface diagram for vehicle accident reporting shows that after the video is latched, a vehicle accident report is automatically generated, displaying the target time (T1) of the risk event, the parking location (L), the vehicle accident risk level (C), the vehicle area (F), the suspected offending vehicle or person (object type E), and the image of the person or object (object feature Y), etc. Then, the vehicle accident report and the target video are sent to the corresponding client.

[0079] Example 2

[0080] Please refer to Figure 3 This is a schematic diagram of the structure of a vehicle accident detection device according to an embodiment of the present invention. The device includes: a real-time vehicle monitoring module, a vehicle video acquisition module, a vehicle area detection module, a vehicle accident risk level determination module, an object feature recognition module, and a vehicle accident report generation module.

[0081] The vehicle real-time monitoring module is used to detect the distance between the moving object and the vehicle when a moving object is detected around the vehicle.

[0082] The vehicle video acquisition module is used to acquire vehicle video during a time period when the distance between the moving object and the vehicle is within a preset threshold when the distance between the moving object and the vehicle is less than a preset threshold, use the vehicle video as the target video, use the time point when the distance between the moving object and the vehicle is the shortest as the target time point, and use the distance when the distance between the moving object and the vehicle is the shortest as the target distance.

[0083] The vehicle area detection module is used to detect the relative position area between the moving object and the vehicle at the target time point, and to use the relative position area as the target area; wherein, the relative position area includes: front left of the vehicle, front right of the vehicle, left side of the vehicle, right side of the vehicle, rear left of the vehicle, and rear right of the vehicle.

[0084] The vehicle accident risk level determination module is used to obtain the vibration of the vehicle based on a preset vehicle vibration sensor, and to determine the vehicle accident risk level based on the vibration and the target distance.

[0085] The object feature recognition module is used to acquire an image of the moving object, extract features from the image, determine the object type of the moving object based on the extracted image features, and identify the object features of the moving object based on the object type; wherein, the object features include: license plate number or human face;

[0086] The vehicle accident report generation module is used to obtain the current parking location of the vehicle, generate a corresponding vehicle accident report based on the parking location, target time point, target area, vehicle accident risk level, object type and object characteristics, and send the vehicle accident report and the target video to the corresponding client.

[0087] Preferably, the vibration status of the vehicle includes: vibration occurring or no vibration occurring; determining the vehicle accident risk level based on the vibration status and the target distance includes: determining the vehicle accident risk level as high-risk when the vehicle vibrates; determining the vehicle accident risk level as medium-risk when the vehicle does not vibrate and the target distance does not exceed a preset first distance threshold; determining the vehicle accident risk level as low-risk when the vehicle does not vibrate, the target distance is greater than the first distance threshold but does not exceed a preset second distance threshold; wherein the first distance threshold is less than the second distance threshold.

[0088] Preferably, the step of extracting features from the image and then determining the object type of the moving object based on the extracted image features includes: extracting image features from the image; wherein the image features include: key pixels, image edges, and image patches; comparing the extracted image features with image features in a preset image feature library, and then determining the object type of the moving object based on the comparison result; wherein the object type includes: motor vehicles, non-motor vehicles, or pedestrians.

[0089] Preferably, the step of identifying the object features of the moving object based on the object type includes: when the object type is a motor vehicle or a non-motor vehicle, extracting the texture, color, and shape from the image, performing feature analysis on the extracted texture, color, and shape, locating the license plate area where the vehicle license plate is located, and performing character recognition on the license plate area to obtain the license plate number of the moving object, and using the extracted license plate number as the object feature of the moving object; when the object type is a pedestrian, extracting the facial information of the moving object from the image according to a preset face detection algorithm, and using the extracted facial information as the object feature of the moving object.

[0090] It should be noted that the device embodiments described above are merely illustrative. 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 the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0091] Those skilled in the art will clearly understand that, for convenience and simplicity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0092] Example 3

[0093] Accordingly, embodiments of the present invention provide an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the vehicle accident detection method described in the above embodiments of the invention.

[0094] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0095] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0096] Example 4

[0097] Accordingly, embodiments of the present invention provide a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the vehicle accident detection method described in the above embodiments of the invention.

[0098] The memory can be used to store the computer program. The processor implements various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart memory card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0099] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0100] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A vehicle accident detection method characterized by, The method comprises the following steps: monitoring whether there is a moving object around the vehicle; when a moving object is detected, detecting the distance between the moving object and the vehicle; when the distance between the moving object and the vehicle is less than a preset threshold, obtaining a vehicle video of a time period when the distance between the moving object and the vehicle is within the preset threshold, taking the vehicle video as a target video, taking a time point when the distance between the moving object and the vehicle is the shortest as a target time point, and taking the distance between the moving object and the vehicle when the distance is the shortest as a target distance; detecting the relative position area of the moving object and the vehicle at the target time point according to the target time point, and taking the relative position area as a target area; wherein the relative position area comprises the left front of the vehicle, the right front of the vehicle, the left side of the vehicle, the right side of the vehicle, the left rear of the vehicle, and the right rear of the vehicle; obtaining the vibration condition of the vehicle according to a preset vehicle vibration sensor; wherein the vibration condition of the vehicle comprises vibration or no vibration; according to the vibration condition and the target distance, when the vehicle vibrates, determining that the vehicle accident risk level is a high risk level; when the vehicle does not vibrate and the target distance is not more than a preset first distance threshold, determining that the vehicle accident risk level is a medium risk level; when the vehicle does not vibrate, the target distance is greater than the first distance threshold and is not more than a preset second distance threshold, determining that the vehicle accident risk level is a low risk level; wherein the first distance threshold is less than the second distance threshold; obtaining an image of the moving object, extracting image features in the image, comparing the extracted image features with image features in a preset image feature library, then determining the object type of the moving object according to the comparison result, and identifying the object features of the moving object according to the object type; wherein the object features comprise a license plate number or a face; the image features comprise key pixels, image edges and image patches; and the object type comprises a motor vehicle, a non-motor vehicle or a pedestrian; 2. The vehicle accident detection method according to claim 1, characterized by, obtaining the current parking location of the vehicle, generating a corresponding vehicle accident report according to the parking location, the target time point, the target area, the vehicle accident risk level, the object type and the object features, and sending the vehicle accident report and the target video to a corresponding client. The method of identifying the object features of the moving object according to the object type comprises: when the object type is a motor vehicle or a non-motor vehicle, extracting the texture, color and shape in the image, performing feature analysis on the extracted texture, color and shape, positioning the license plate area where the vehicle license plate is located, performing character recognition on the license plate area, obtaining the license plate number of the moving object, and taking the extracted license plate number as the object features of the moving object; 3. A vehicle accident detection apparatus characterized by comprising: when the object type is a pedestrian, extracting the face information of the moving object from the image according to a preset face detection algorithm, and taking the extracted face information as the object features of the moving object. The method comprises the following steps: The vehicle real-time monitoring module, the vehicle video acquisition module, the vehicle area detection module, the vehicle accident risk level determination module, the object feature recognition module, and the vehicle accident report generation module; The vehicle real-time monitoring module is configured to detect a distance between the moving object and the vehicle when the vehicle detects a moving object around the vehicle; The vehicle video acquisition module is configured to acquire a vehicle video of a time period when the distance between the moving object and the vehicle is within a preset threshold, take the vehicle video as a target video, take a time point when the distance between the moving object and the vehicle is the shortest as a target time point, and take the distance between the moving object and the vehicle when the distance is the shortest as a target distance, when the distance between the moving object and the vehicle is less than the preset threshold; The vehicle area detection module is configured to detect a relative position area of the moving object and the vehicle at the target time point according to the target time point, and take the relative position area as a target area; the relative position area includes a left front of the vehicle, a right front of the vehicle, a left side of the vehicle, a right side of the vehicle, a left rear of the vehicle, and a right rear of the vehicle; The vehicle accident risk level determination module is configured to acquire a vibration condition of the vehicle according to a preset vehicle vibration sensor; the vibration condition of the vehicle includes vibration or no vibration; According to the vibration condition and the target distance, when the vehicle vibrates, the vehicle accident risk level is determined as a high risk level; when the vehicle does not vibrate and the target distance is not more than a preset first distance threshold, the vehicle accident risk level is determined as a medium risk level; when the vehicle does not vibrate, the target distance is greater than the first distance threshold and is not more than a preset second distance threshold, the vehicle accident risk level is determined as a low risk level; the first distance threshold is less than the second distance threshold; The object feature recognition module is configured to acquire an image of the moving object, extract an image feature in the image, compare the extracted image feature with an image feature in a preset image feature library, determine an object type of the moving object according to a comparison result, and identify an object feature of the moving object according to the object type; the object feature includes a license plate number or a face; the image feature includes a key pixel, an image edge, and an image patch; and the object type includes a motor vehicle, a non-motor vehicle, or a pedestrian; The vehicle accident report generation module is configured to acquire a current parking location of the vehicle, generate a corresponding vehicle accident report according to the parking location, the target time point, the target area, the vehicle accident risk level, the object type, and the object feature, and send the vehicle accident report and the target video to a corresponding client.

4. The vehicle crash detection apparatus of claim 3 wherein, The identification of the object feature of the moving object according to the object type includes: When the object type is a motor vehicle or a non-motor vehicle, the texture, color and shape in the image are extracted, the extracted texture, color and shape are analyzed for features, the license plate area where the vehicle license plate is located is located, and the license plate area is used for character recognition to obtain the license plate number of the moving object. The extracted license plate number is used as the object feature of the moving object. When the object type is a pedestrian, the facial information of the moving object is extracted from the image according to a preset face detection algorithm, and the extracted facial information is used as the object feature of the moving object.

5. An electronic device, comprising: The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the vehicle accident detection method as described in any one of claims 1 to 2.

6. A storage medium, characterized by The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the vehicle accident detection method as described in any one of claims 1 to 2.

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