WO2022078353A1

WO2022078353A1 - Vehicle driving state determination method and apparatus, and computer device and storage medium

Info

Publication number: WO2022078353A1
Application number: PCT/CN2021/123413
Authority: WO
Inventors: 蒋磊
Original assignee: 深圳壹账通智能科技有限公司
Priority date: 2020-10-14
Filing date: 2021-10-13
Publication date: 2022-04-21
Also published as: CN112185131A

Abstract

A vehicle driving state determination method and apparatus, and a computer device and a storage medium, which relate to the technical field of image detection by using artificial intelligence. The method comprises: acquiring a vehicle driving video, extracting continuous key frames therefrom, and calculating driving speeds of a vehicle according to two adjacent key frames (S102); calculating accelerations of the vehicle according to the vehicle speeds of the two adjacent key frames (S103); performing discrete data fitting analysis on the accelerations of the key frames, so as to obtain an acceleration line (S104); determining whether the slope of the acceleration line is less than a preset abnormal threshold value (S51); and if the slope is less than the abnormal threshold value, determining that a driving state of the vehicle is abnormal (S52). The present invention also relates to blockchain technology, and information can be stored in a blockchain node. An abnormal driving state of a vehicle is intelligently identified, and the situations in the prior art of poor timeliness and inaccurate information of a traffic accident case report, and false case reports are solved.

Description

Vehicle driving state judgment method, device, computer equipment and storage medium

This application claims the priority of the Chinese patent application filed on October 14, 2020 with the application number CN202011096024.3 and the title of “Method, Device, Computer Equipment and Storage Medium for Judging Vehicle Running State”, the entire contents of which are incorporated by reference in this application.

technical field

The present application relates to the technical field of image detection of artificial intelligence, and in particular, to a method, device, computer equipment and storage medium for judging the driving state of a vehicle, which use the computer vision technology of artificial intelligence.

Background technique

At present, the key roads and areas in China have been covered by the Sky Eye system monitoring, and the traffic flow data has been photographed and recorded. However, this surveillance video data is not used to automatically determine whether there is a car accident, but to actively report the accident alarm through the parties to the accident or passers-by.

However, the inventor realizes that through the active reporting of the parties, there will be several deficiencies: 1. The timeliness is poor. For example, the time from the occurrence of a car accident to the successful report is precious rescue time and processing time; 2. The information is inaccurate, For example, a telephone report requires the police and the reporting party to repeatedly confirm the time and location information, and the case information provided by the reporting party is often inaccurate; 3. There are cases of false reports.

SUMMARY OF THE INVENTION

The purpose of this application is to provide a method, device, computer equipment and storage medium for judging the driving state of a vehicle, which are used to solve the problems of poor timeliness, inaccurate information and false reporting of car accident reports in the prior art; this application can be applied to In smart traffic scenarios, to promote the construction of smart cities.

In order to achieve the above purpose, the present application provides a method for judging the driving state of a vehicle, including:

Obtain the vehicle driving video and extract the consecutive key frames, calculate the driving speed of the vehicle according to the adjacent two key frames, and mark the vehicle information and driving speed of the vehicle on the next key frame of the two key frames. ;

Calculate the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames;

Performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame;

Determine whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, determine that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, determine that the vehicle The driving state is normal.

In order to achieve the above purpose, the present application also provides a vehicle driving state judging device, comprising:

The speed calculation module is used to obtain the vehicle driving video and extract the consecutive key frames, calculate the driving speed of the vehicle according to the adjacent two key frames, and mark the vehicle information and driving speed of the vehicle in the two key frames. on the next keyframe of ;

an acceleration calculation module, configured to calculate the acceleration of the vehicle according to the vehicle speed of two adjacent key frames, and mark the acceleration on the next key frame of the two key frames;

a line formulating module, configured to perform discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame;

The state judgment state is used to judge whether the slope of the acceleration line is lower than the preset abnormal threshold; if it is lower than the abnormal threshold, it is determined that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, Then it is determined that the running state of the vehicle is normal.

In order to achieve the above object, the present application also provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, which is implemented when the processor of the computer device executes the computer program. The above-mentioned method for judging the driving state of a vehicle, the method for judging the driving state of the vehicle includes:

In order to achieve the above object, the present application also provides a computer storage medium, where a computer program is stored on the storage medium, and when the computer program stored in the storage medium is executed by a processor, the above-mentioned vehicle driving state judgment method is realized, and the The method of judging the driving state of the vehicle, including:

The method, device, computer equipment and storage medium for judging the driving state of the vehicle provided by the present application, by calculating the driving speed of the vehicle, and obtaining the acceleration of the vehicle according to the vehicle speed of two adjacent key frames, and then for each key frame. The acceleration of the frame is subjected to discrete data fitting analysis to obtain the acceleration line, which realizes the control of the acceleration change generated by the vehicle information with the passage of the key frame, and realizes by judging whether the slope of the acceleration line is lower than the preset abnormal threshold. The determination of the driving state of the vehicle realizes the intelligent identification of the abnormal driving state of the vehicle, and solves the problems of poor timeliness, inaccurate information and false reporting of a car accident in the prior art.

Description of drawings

FIG. 1 is a flow chart of Embodiment 1 of the method for judging the driving state of a vehicle according to the application;

2 is a schematic diagram of the environmental application of the method for judging the driving state of a vehicle in Embodiment 2 of the method for judging the driving state of the vehicle;

FIG. 3 is a specific method flow chart of the vehicle driving state judgment method in Embodiment 2 of the vehicle driving state judgment method of the present application;

4 is a flowchart of a specific method for calculating the driving speed of a vehicle according to two adjacent key frames in Embodiment 2 of the method for judging the driving state of a vehicle of the present application, and marking the vehicle information and driving speed of the vehicle on the key frames;

5 is a flowchart of a specific method for calculating the acceleration of a vehicle according to the vehicle speed of two adjacent key frames in Embodiment 2 of the method for judging the driving state of a vehicle of the present application, and marking the acceleration on the key frames;

6 is a flowchart of a specific method for obtaining acceleration lines by performing discrete data fitting analysis on the acceleration of each key frame in Embodiment 2 of the vehicle driving state judgment method of the present application;

FIG. 7 is a schematic diagram of a program module of Embodiment 3 of the apparatus for judging the driving state of a vehicle according to the application;

FIG. 8 is a schematic diagram of a hardware structure of a computer device in Embodiment 4 of the computer device of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

The vehicle driving state judgment method, device, computer equipment and storage medium provided by this application are suitable for the field of artificial intelligence image detection technology, and provide a vehicle based on a speed calculation module, an acceleration calculation module, a line drawing module and a state judgment state. Use the state judgment method. In the present application, the vehicle driving video is acquired and the consecutive key frames are extracted, the driving speed of the vehicle is calculated according to the adjacent two key frames, and the vehicle information and driving speed of the vehicle are marked in the latter one of the two key frames. On the key frame; calculate the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames; perform discrete data fitting on the acceleration of each key frame Analyzing and obtaining an acceleration line, the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame; judging whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold , then it is determined that the running state of the vehicle is abnormal; if it is not lower than the abnormal threshold, it is determined that the running state of the vehicle is normal.

Example 1:

Referring to FIG. 1 , a method for judging the driving state of a vehicle in this embodiment includes:

S102: Acquire the video of the vehicle driving and extract the consecutive key frames therein, calculate the driving speed of the vehicle according to the adjacent two key frames, and mark the vehicle information and driving speed of the vehicle on the next key frame of the two key frames on the frame.

S103 : Calculate the acceleration of the vehicle according to the vehicle speeds of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames.

S104: Perform discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame.

S105: Determine whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, determine that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, determine the The driving state of the vehicle is normal.

In an exemplary embodiment, the driving speed of the vehicle is calculated by extracting consecutive key frames in the vehicle driving video, and according to the relative displacement of the vehicle in two adjacent key frames, and the vehicle information of the vehicle and the vehicle information are calculated. The driving speed is marked on the next key frame of the two key frames, so as to obtain the speed of the running vehicle, so as to accurately obtain the effect of the acceleration of the vehicle;

The acceleration of the vehicle is obtained by calculating the vehicle speed of the vehicle in two adjacent key frames, and the acceleration is marked on the next key frame of the two key frames, so as to obtain the acceleration of the running vehicle, In order to accurately grasp the driving state of the vehicle, that is, the effect of normal (such as: normal driving) or abnormal (such as: rear-end collision, collision, etc.);

Acceleration lines are obtained by discrete data fitting analysis of the acceleration of each key frame, which can control the acceleration change of vehicle information with the passage of key frames, and then help to judge whether the driving state of the vehicle is normal or not. Effect;

By judging whether the slope of the acceleration line is lower than a preset abnormal threshold, the determination of the driving state of the vehicle is realized, the driving state of the vehicle is determined according to the acceleration line, and the technical effect of intelligently identifying the driving state of the vehicle is realized; for example: a normal vehicle The maximum acceleration of the vehicle is 0.6g, the maximum acceleration of the vehicle's sudden braking is -1g, and when the vehicle collides or rear-ends, its acceleration can reach -30g or even -50g within 20ms. Therefore, once a collision occurs Or in the case of rear-end collision, the acceleration line obtained by the least squares method will cause the slope of the acceleration line to decrease abruptly due to the sharply reduced acceleration. Whether there is an abnormal situation such as collision or rear-end collision, the technical effect of intelligently identifying vehicle anomalies (collision, rear-end collision, etc.) is realized.

Therefore, the present application solves the problems of poor timeliness, inaccurate information, and false reports existing in the prior art by realizing intelligent identification of abnormal vehicle driving states.

The present application can be applied in smart traffic scenarios, thereby promoting the construction of smart cities.

Wherein, the S105 shown in Fig. 1 is shown in the following marked form:

S51: Determine whether the slope of the acceleration line is lower than a preset abnormal threshold;

S52: If it is lower than the abnormal threshold, determine that the driving state of the vehicle is abnormal;

S53: If it is not lower than the abnormality threshold, determine that the driving state of the vehicle is normal.

Embodiment 2:

This embodiment is a specific application scenario of the above-mentioned Embodiment 1. Through this embodiment, the method provided in this application can be described more clearly and specifically.

Next, in the server running the vehicle driving state judgment method, the acceleration of the vehicle is calculated for the vehicle speed of two adjacent key frames, and the acceleration line of each key frame is obtained by discrete data fitting analysis. The method provided by this embodiment is described in detail by taking the slope of the acceleration line as an example to determine the driving state of the vehicle. It should be noted that this embodiment is only exemplary, and does not limit the protection scope of the embodiment of this application.

FIG. 2 schematically shows a schematic diagram of an environmental application of the method for judging the driving state of a vehicle according to the second embodiment of the present application.

In an exemplary embodiment, the authentication server 2 where the vehicle running state determination method is located is connected to the photographing device 4 through a network 3, the server 2 may provide services through one or more networks 3, and the network 3 may include various network devices, For example routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, proxy devices and/or the like. The network 3 may include physical links such as coaxial cable links, twisted pair cable links, fiber optic links, combinations thereof, and/or the like. The network 3 may include wireless links, such as cellular links, satellite links, Wi-Fi links and/or the like; the shooting device 4 is a video shooting system, such as a sky-eye system, CCTV and other computer equipment.

FIG. 3 is a flowchart of a specific method of a method for judging a driving state of a vehicle provided by an embodiment of the present application, and the method specifically includes steps S201 to S205.

S201 : Establish a communication connection with a preset photographing device to acquire in real time the driving video of the vehicle photographed by the photographing device.

In this step, the Sky Eye system can be used as the shooting device. The Sky Eye system is a kind of network video system, which usually refers to the IP surveillance system used for specific applications in the field of security monitoring and remote monitoring. IP network (LAN/WAN/Internet) realizes video surveillance, video image recording, and related alarm management. In this embodiment, the sky-eye system is used to shoot a driving video of a moving vehicle.

S202: Acquire a video of the vehicle driving and extract consecutive key frames therein, calculate the driving speed of the vehicle according to two adjacent key frames, and mark the vehicle information and driving speed of the vehicle on the next key frame of the two key frames on the frame.

In order to obtain the speed of the vehicle in motion, so as to obtain the acceleration of the vehicle accurately, in this step, the continuous key frames in the vehicle driving video are extracted, and the relative displacement of the vehicle in two adjacent key frames is calculated. The driving speed of the vehicle, and the vehicle information and driving speed of the vehicle are marked on the next key frame of the two key frames.

Preferably, in order to ensure that the server can accurately obtain the acceleration of the vehicle at each stage of actual driving, so as to accurately grasp the driving state of the vehicle, and at the same time to avoid excessive computing burden on the server due to too frequent acquisition and calculation of key frames, this step passes The key frames are continuously extracted from the vehicle driving video according to the preset extraction interval and extraction quantity.

In this embodiment, the extraction interval and extraction quantity can be set as required. For example, if the extraction interval is 40ms and the extraction quantity is 1000, then 1000 key frames are extracted from the vehicle driving video at an interval of 40ms.

In a preferred embodiment, referring to FIG. 4 , the driving speed of the vehicle is calculated according to two adjacent key frames, and the vehicle information and driving speed of the vehicle are marked in the next key frame of the two key frames. steps above, including:

S21: Identify vehicles appearing in each key frame, and mark the vehicle information of the vehicle on the key frame.

In this step, the license plate of the vehicle that appears in each key is identified by the vehicle license plate recognition technology, so as to realize the purpose of identifying the vehicle that appears; The purpose of marking the vehicle.

It should be noted that image vehicle recognition can be converted into image license plate recognition, and the acceleration of the vehicle motion trajectory is obtained through the acceleration of the license plate. There are many algorithms for vehicle license plate recognition, such as OCR technology. In this solution, the algorithm is only applied without in-depth analysis.

S22: Calculate the traveling speed of each vehicle according to the relative displacement of the vehicle in two adjacent key frames.

In this step, according to the relative displacement of the license plate in the two adjacent key frames and the extraction interval, calculate the driving speed of the license plate between the two adjacent key frames, and set it as is the speed of the vehicle.

Exemplarily, if the relative displacement of the license plate "Shanghai A*****" in two key frames is 1m and the extraction interval is 40ms, then the obtained travel speed is 1m/40ms=25m/s=90km/h.

It should be noted that the relative displacement is obtained by measuring the position of the vehicle in the two key frames by a distance metric method, which is a mathematical law used to measure the distance along the curve and The angle between the curves includes the information of the curvature of the space where the curves are located; since the technical problem to be solved in this step is how to obtain the acceleration of the vehicle according to the speed of the vehicle, which is helpful to judge the driving state of the vehicle; therefore, the distance measurement The specific technical principle is not repeated here.

S23: Mark the traveling speed in the next key frame of the two adjacent key frames, and associate the traveling speed with the vehicle information of the vehicle.

Exemplarily, based on the above example, assuming that two adjacent key frames include key frame A and key frame B, wherein key frame B is located after key frame A, then, the vehicle information: "Shanghai A*** **”, the travel speed: 25m/s, 90km/h is marked on key frame B.

S203 : Calculate the acceleration of the vehicle according to the vehicle speeds of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames.

In order to obtain the acceleration of the vehicle in motion, so as to accurately grasp the driving state of the vehicle, namely: normal (such as: normal driving) or abnormal (such as: rear-end collision, collision, etc.) The vehicle speed of the two key frames is used to obtain the acceleration of the vehicle, and the acceleration is marked on the next key frame of the two key frames.

In a preferred embodiment, referring to FIG. 5, the acceleration of the vehicle is calculated according to the vehicle speed of two adjacent key frames, and the acceleration is marked on the next key frame of the two key frames, include:

S31 : Extract the driving speed of the same vehicle information in two adjacent key frames, and calculate the driving speed of the two key frames to obtain the acceleration.

S32: Mark the acceleration in the next key frame of the two adjacent key frames, and associate the acceleration with the vehicle information.

Exemplarily, based on the above example, it is assumed that two adjacent key frames include key frame B and key frame C, wherein key frame C is located after key frame B; key frame B is marked with vehicle information: "Shanghai A* ****” , travel speed: 25m/s; key frame C is marked with vehicle information: "Shanghai A*****", travel speed: 24.9m/s; and the extraction interval is 40m/s, then the obtained acceleration is -2.5m /s2.

S204: Perform discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame.

In order to realize the technical effect of controlling the acceleration changes generated by the vehicle information with the passage of key frames, and then help to judge whether the driving state of the vehicle is normal, this step is performed by discrete data fitting of the acceleration of each key frame. The acceleration line is obtained by analysis; wherein, the discrete data fitting analysis is a representation method in which existing data is substituted into a mathematical formula through a mathematical method. For scientific and engineering problems, several discrete data can be obtained through methods such as sampling and experiments. According to these data, we often hope to obtain a continuous function (that is, a curve) or a denser discrete equation that is consistent with the known data. This process It's called fitting.

In this embodiment, the least squares method is used to linearly fit the acceleration of each key frame as discrete data, so as to perform discrete data fitting analysis on the acceleration of each key frame and obtain the acceleration line.

It should be noted that the least squares method is a mathematical optimization technique that finds the best functional match for the data by minimizing the sum of squared errors. The unknown data can be easily obtained by the least squares method, and the sum of squares of the errors between the obtained data and the actual data can be minimized. In this embodiment, the Matlab module implements the least squares method to perform discrete data fitting analysis on the acceleration of each key frame.

In a preferred embodiment, referring to FIG. 6 , the steps of performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line include:

S41: According to the sequence of the consecutive key frames, sequentially extract the acceleration of the same vehicle information in each of the key frames.

S42: Formulate discrete points of the vehicle information on the key frame according to the order of the key frame in the sequence and the acceleration of the key frame.

In this step, any key frame in the continuous key frames is used as the target frame, the order of the target frame in the continuous key frames is used as the abscissa, and the acceleration of the target frame is used as the ordinate , to obtain discrete points of the vehicle information on the target frame.

S43: Using the least squares method to calculate discrete points of the vehicle information on each key frame to obtain an acceleration line, where the acceleration line fits an acceleration change of the vehicle information with the passage of the key frame.

Exemplarily, it is assumed that the obtained consecutive key frames include: key frame 1, key frame 2, key frame 3, key frame 4, and key frame 5;

Extract the same vehicle information in each key frame, such as: Shanghai A*****, the accelerations are: key frame 1, acceleration: -2.5m/s2; key frame 2, acceleration: -2.1m/s2; key frame 3. Acceleration: -1.4m/s2; keyframe 4, acceleration: -1m/s2; keyframe 5, acceleration: -0.6m/s2; then according to the least square method, the acceleration line of y=0.5x-3 will be obtained .

Preferably, after the acceleration line is obtained by performing discrete data fitting analysis on the acceleration of each key frame, the method further includes:

Upload the acceleration line to the blockchain.

It should be noted that the corresponding summary information is obtained based on the acceleration lines. Specifically, the summary information is obtained by hashing the acceleration lines, for example, by using the sha256s algorithm. Uploading summary information to the blockchain ensures its security and fairness and transparency to users. The user equipment can download the summary information from the blockchain to verify whether the acceleration line has been tampered with. The blockchain referred to in this example is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

S205: Determine whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, determine that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, determine the The driving state of the vehicle is normal.

In order to realize the technical effect of judging the driving state of the vehicle according to the acceleration line, thereby realizing the technical effect of intelligently recognizing the driving state of the vehicle, this step realizes the judgment of the driving state of the vehicle by judging whether the slope of the acceleration line is lower than the preset abnormal threshold; usually The maximum acceleration of the vehicle is 0.6g, the maximum acceleration of the vehicle sudden braking is -1g, and when the vehicle collides or rear-ends, its acceleration can reach -30g or even -50g within 20ms. In the event of a collision or rear-end collision, the acceleration line obtained by the least squares method will cause the slope of the acceleration line to decrease abruptly due to the sharply reduced acceleration. Therefore, by comparing the slope with the preset abnormal threshold, it can be determined that Whether the vehicle has an abnormal situation such as collision or rear-end collision, it realizes the technical effect of intelligently identifying vehicle anomalies (collision, rear-end collision, etc.).

Wherein, the S205 in FIG. 3 is shown in the following labeling form:

Exemplarily, based on the above example, an acceleration line of y=0.5x-3 is obtained, and its slope is 0.5; if the abnormal threshold is -5, it is determined that the slope of the acceleration line is not lower than the abnormal threshold, and then It is determined that the driving state of the vehicle is normal; if the obtained acceleration line is y=-10x-6 (in the event of a rear-end collision or a collision, the slope of the acceleration line will drop suddenly due to the sudden drop in acceleration), if the If the abnormality threshold is still -5, it is determined that the slope of the acceleration line is lower than the abnormality threshold, and then it is determined that the driving state of the vehicle is abnormal.

Embodiment three:

Referring to FIG. 7 , a vehicle driving state judging device 1 of the present embodiment includes:

The speed calculation module 12 is used for acquiring the vehicle driving video and extracting consecutive key frames therein, calculating the driving speed of the vehicle according to the adjacent two key frames, and marking the vehicle information and driving speed of the vehicle in the two key frames. on the next keyframe of the frame;

The acceleration calculation module 13 is used to calculate the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames;

The line formulating module 14 is used to perform discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, and the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame;

The state judgment state 15 is used to judge whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, it is determined that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold , then it is determined that the running state of the vehicle is normal.

Optionally, the vehicle driving state judging device 1 further includes:

The communication connection module 11 is configured to establish a communication connection with a preset photographing device, so as to obtain the driving video of the vehicle photographed by the photographing device in real time.

The technical solution relates to the technical field of artificial intelligence image detection. The distance measurement method in the image matching technology is used to obtain the vehicle driving video and extract the continuous key frames. The driving speed of the vehicle is calculated according to the adjacent two key frames. Calculate the acceleration of the vehicle from the vehicle speed of the two keyframes, Discrete data fitting analysis is performed on the acceleration of each key frame to obtain an acceleration line, and it is determined whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, it is determined that the driving state of the vehicle is abnormal.

Embodiment 4:

In order to achieve the above purpose, the present application also provides a computer device 5. The components of the vehicle driving state judging device 1 of the third embodiment can be dispersed in different computer devices, and the computer device 5 can be a smart phone or a tablet computer that executes programs. , notebook computers, desktop computers, rack servers, blade servers, tower servers or rack servers (including independent servers, or server clusters composed of multiple application servers), etc. The computer device in this embodiment at least includes, but is not limited to, a memory 51 and a processor 52 that can be communicatively connected to each other through a system bus, as shown in FIG. 8 . It should be pointed out that FIG. 8 only shows a computer device having a component -, but it should be understood that it is not required to implement all the shown components, and more or less components may be implemented instead.

In this embodiment, the memory 51 (ie, the storage medium) includes flash memory, hard disk, multimedia card, card-type memory (eg, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM), Magnetic Memory, Magnetic Disk, Optical Disc, etc. In some embodiments, the memory 51 may be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory 51 may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), Secure Digital (SD) card, Flash Card (Flash Card), etc. Of course, the memory 51 may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory 51 is generally used to store the operating system and various application software installed on the computer equipment, such as the program code of the vehicle driving state judging device of the third embodiment. In addition, the memory 51 can also be used to temporarily store various types of data that have been output or will be output.

The processor 52 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 52 is typically used to control the overall operation of the computer device. In this embodiment, the processor 52 is used for running the program code or processing data stored in the memory 51 , for example, running the vehicle running state judging device, so as to realize the vehicle running state judging methods of the first and second embodiments.

Embodiment 5:

In order to achieve the above purpose, the present application also provides a computer storage medium, the computer-readable storage medium may be non-volatile or volatile, such as flash memory, hard disk, multimedia card, card-type storage (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), Magnetic storage, magnetic disk, optical disk, server, App application mall, etc., on which computer programs are stored, and when the programs are executed by the processor 52, corresponding functions are realized. The computer storage medium of this embodiment is used to store the vehicle running state judging device, and when executed by the processor 52, implements the vehicle running state judging methods of the first and second embodiments.

The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

From the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation.

The above are only the preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present application, or directly or indirectly applied in other related technical fields , are similarly included within the scope of patent protection of this application.

Claims

A method for judging the driving state of a vehicle, comprising:

Obtain the vehicle driving video and extract the consecutive key frames, calculate the driving speed of the vehicle according to the adjacent two key frames, and mark the vehicle information and driving speed of the vehicle on the next key frame of the two key frames. ;

Calculate the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames;

Performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame;

Determine whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, determine that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, determine that the vehicle The driving state is normal.
The method for judging the driving state of a vehicle according to claim 1, wherein before acquiring the driving video of the vehicle, the method comprises:

A communication connection is established with a preset photographing device, so as to obtain the driving video of the vehicle photographed by the photographing device in real time.
The method for judging the driving state of a vehicle according to claim 1, wherein acquiring the driving video of the vehicle and extracting continuous key frames therein comprises:

The key frames are continuously extracted from the vehicle driving video according to the preset extraction interval and extraction quantity.
The method for judging the driving state of a vehicle according to claim 1, wherein the driving speed of the vehicle is calculated according to two adjacent key frames, and the vehicle information and the driving speed of the vehicle are marked in the latter one of the two key frames. Steps on keyframes, including:

Identifying vehicles appearing in each key frame, and marking the vehicle information of the vehicle on the key frames;

Calculate the driving speed of each vehicle according to the relative displacement of the vehicle in two adjacent key frames;

The traveling speed is marked in the next key frame of the two adjacent key frames, and the traveling speed and the vehicle information of the vehicle are associated.
The method for judging the driving state of a vehicle according to claim 1, wherein the acceleration of the vehicle is calculated according to the vehicle speed of two adjacent key frames, and the acceleration is marked on the next key frame of the two key frames. steps, including:

Extracting the driving speed of the same vehicle information in two adjacent key frames, and calculating the driving speed of the two key frames to obtain the acceleration;

The acceleration is marked in the next key frame of the two adjacent key frames, and the acceleration is associated with the vehicle information.
The method for judging the driving state of a vehicle according to claim 1, wherein the acceleration lines obtained by performing discrete data fitting analysis on the acceleration of each key frame include:

The acceleration of each key frame as discrete data is linearly fitted by the least squares method, so as to implement discrete data fitting analysis on the acceleration of each of the key frames and obtain the acceleration line.
The method for judging the driving state of a vehicle according to claim 1, wherein the step of performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line comprises:

According to the sequence of the consecutive key frames, sequentially extract the acceleration of the same vehicle information in each of the key frames;

formulating discrete points of the vehicle information on the key frame according to the rank of the key frame in the sequence and the acceleration of the key frame;

Using the least squares method to calculate the discrete points of the vehicle information on each key frame to obtain an acceleration line, the acceleration line fitting the acceleration change of the vehicle information with the passage of the key frame;

After performing discrete data fitting analysis on the acceleration of each key frame to obtain the acceleration line, it also includes:

Upload the acceleration lines to the blockchain.
A vehicle driving state judging device, comprising:

The speed calculation module is used to obtain the vehicle driving video and extract the consecutive key frames, calculate the driving speed of the vehicle according to the adjacent two key frames, and mark the vehicle information and driving speed of the vehicle in the two key frames. on the next keyframe of ;

an acceleration calculation module, configured to calculate the acceleration of the vehicle according to the vehicle speed of two adjacent key frames, and mark the acceleration on the next key frame of the two key frames;

a line formulating module, configured to perform discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame;

The state judgment state is used to judge whether the slope of the acceleration line is lower than the preset abnormal threshold; if it is lower than the abnormal threshold, it is determined that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, Then it is determined that the running state of the vehicle is normal.
A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor of the computer device implements the method for judging the driving state of the vehicle when the computer program is executed , the steps of the method for judging the driving state of the vehicle include:

Obtain the vehicle driving video and extract the consecutive key frames, calculate the driving speed of the vehicle according to the adjacent two key frames, and mark the vehicle information and driving speed of the vehicle on the next key frame of the two key frames. ;

Calculate the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames;

Performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame;

Determine whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, determine that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, determine that the vehicle The driving state is normal.
The computer device according to claim 9, wherein before acquiring the vehicle driving video, it comprises:

establishing a communication connection with a preset shooting device to obtain the driving video of the vehicle shot by the shooting device in real time;

Obtaining vehicle driving video and extracting consecutive key frames include:

The key frames are continuously extracted from the vehicle driving video according to the preset extraction interval and extraction quantity.
The computer device according to claim 9, wherein the driving speed of the vehicle is calculated according to two adjacent key frames, and the vehicle information and the driving speed of the vehicle are marked on the next key frame of the two key frames steps, including:

Identifying vehicles appearing in each key frame, and marking the vehicle information of the vehicle on the key frames;

Calculate the driving speed of each vehicle according to the relative displacement of the vehicle in two adjacent key frames;

The traveling speed is marked in the next key frame of the two adjacent key frames, and the traveling speed and the vehicle information of the vehicle are associated.
The computer device according to claim 9, wherein the step of calculating the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and marking the acceleration on the next key frame of the two key frames, comprises: :

Extracting the driving speed of the same vehicle information in two adjacent key frames, and calculating the driving speed of the two key frames to obtain the acceleration;

The acceleration is marked in the next key frame of the two adjacent key frames, and the acceleration is associated with the vehicle information.
The computer device according to claim 9, wherein, performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line comprising:

The acceleration of each key frame as discrete data is linearly fitted by the least squares method, so as to implement discrete data fitting analysis on the acceleration of each of the key frames and obtain the acceleration line.
The computer device according to claim 9, wherein the step of performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, comprising:

According to the sequence of the consecutive key frames, sequentially extract the acceleration of the same vehicle information in each of the key frames;

formulating discrete points of the vehicle information on the key frame according to the rank of the key frame in the sequence and the acceleration of the key frame;

Using the least squares method to calculate the discrete points of the vehicle information on each key frame to obtain an acceleration line, the acceleration line fitting the acceleration change of the vehicle information with the passage of the key frame;

After performing discrete data fitting analysis on the acceleration of each key frame to obtain the acceleration line, it also includes:

Upload the acceleration lines to the blockchain.
A computer storage medium on which a computer program is stored, wherein the computer program stored in the storage medium is executed by a processor to realize the method for judging the running state of the vehicle, and the method for judging the running state of the vehicle is steps, including:

Obtain the vehicle driving video and extract the consecutive key frames, calculate the driving speed of the vehicle according to the adjacent two key frames, and mark the vehicle information and driving speed of the vehicle on the next key frame of the two key frames. ;

Calculate the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and mark the acceleration on the next key frame of the two key frames;

Performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, where the acceleration line is a discrete data fitting line formulated according to the acceleration of each key frame;

Determine whether the slope of the acceleration line is lower than a preset abnormal threshold; if it is lower than the abnormal threshold, determine that the driving state of the vehicle is abnormal; if it is not lower than the abnormal threshold, determine that the vehicle The driving state is normal.
The computer storage medium according to claim 15, wherein before acquiring the vehicle driving video comprises:

establishing a communication connection with a preset shooting device to obtain the driving video of the vehicle shot by the shooting device in real time;

Obtaining vehicle driving video and extracting consecutive key frames include:

The key frames are continuously extracted from the vehicle driving video according to the preset extraction interval and extraction quantity.
The computer storage medium according to claim 15, wherein the driving speed of the vehicle is calculated according to two adjacent key frames, and the vehicle information and the driving speed of the vehicle are marked in a key frame after the two key frames steps above, including:

Identifying vehicles appearing in each key frame, and marking the vehicle information of the vehicle on the key frames;

Calculate the driving speed of each vehicle according to the relative displacement of the vehicle in two adjacent key frames;

The traveling speed is marked in the next key frame of the two adjacent key frames, and the traveling speed and the vehicle information of the vehicle are associated.
The computer storage medium according to claim 15, wherein the step of calculating the acceleration of the vehicle according to the vehicle speed of the two adjacent key frames, and marking the acceleration on the next key frame of the two key frames, include:

Extracting the driving speed of the same vehicle information in two adjacent key frames, and calculating the driving speed of the two key frames to obtain the acceleration;

The acceleration is marked in the next key frame of the two adjacent key frames, and the acceleration is associated with the vehicle information.
The computer storage medium according to claim 15, wherein, performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line comprising:

The acceleration of each key frame as discrete data is linearly fitted by the least squares method, so as to implement discrete data fitting analysis on the acceleration of each of the key frames and obtain the acceleration line.
The computer storage medium according to claim 15, wherein the step of performing discrete data fitting analysis on the acceleration of each key frame to obtain an acceleration line, comprising:

According to the sequence of the consecutive key frames, sequentially extract the acceleration of the same vehicle information in each of the key frames;

formulating discrete points of the vehicle information on the key frame according to the rank of the key frame in the sequence and the acceleration of the key frame;

Using the least squares method to calculate the discrete points of the vehicle information on each key frame to obtain an acceleration line, the acceleration line fitting the acceleration change of the vehicle information with the passage of the key frame;

After the acceleration line is obtained by performing discrete data fitting analysis on the acceleration of each key frame, it also includes:

Upload the acceleration lines to the blockchain.