CN113593218A - Method and device for detecting traffic abnormal event, electronic equipment and storage medium - Google Patents

Method and device for detecting traffic abnormal event, electronic equipment and storage medium Download PDF

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Publication number
CN113593218A
CN113593218A CN202110722070.8A CN202110722070A CN113593218A CN 113593218 A CN113593218 A CN 113593218A CN 202110722070 A CN202110722070 A CN 202110722070A CN 113593218 A CN113593218 A CN 113593218A
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traffic
target time
detected
time period
determining
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CN113593218B (en
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叶锦
谭啸
孙昊
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications

Abstract

The disclosure provides a method and a device for detecting traffic abnormal events, electronic equipment and a storage medium, and relates to the field of artificial intelligence technology and deep learning. The method for detecting the traffic abnormal event comprises the following steps: acquiring N pieces of track information of N vehicles corresponding to a road section to be detected in a target time period, wherein N is a positive integer greater than 1; determining traffic state description information corresponding to the road section to be detected in the target time period according to the N pieces of track information; and determining whether the road section to be detected has a traffic abnormal event in the target time period or not according to the traffic state description information. The method and the device determine the traffic state of the road section according to the track information of the vehicles on the road section to be detected, and further determine whether a traffic abnormal event occurs according to the traffic state. Therefore, the detection and monitoring of the traffic abnormal event are realized.

Description

Method and device for detecting traffic abnormal event, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to the field of artificial intelligence and deep learning technology.
Background
At present, urban road traffic networks are gradually improved, and great convenience is provided for people to travel. However, with the gradual increase of the traffic flow and the pedestrian flow, abnormal conditions such as traffic accidents and traffic jams are easily caused, and further, the traffic efficiency is greatly reduced. Therefore, in order to take measures in time when abnormal traffic conditions occur, it is important to detect and monitor abnormal events in traffic scenes.
Disclosure of Invention
The disclosure provides a method and a device for detecting traffic abnormal events, electronic equipment and a storage medium.
According to a first aspect of the present disclosure, there is provided a method for detecting a traffic abnormal event, including:
acquiring N pieces of track information of N vehicles corresponding to a road section to be detected in a target time period, wherein N is a positive integer greater than 1;
determining traffic state description information corresponding to the road section to be detected in the target time period according to the N pieces of track information;
and determining whether the road section to be detected has a traffic abnormal event in the target time period or not according to the traffic state description information.
According to a second aspect of the present disclosure, there is provided a traffic abnormality event detection apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring N track information of N vehicles corresponding to a road section to be detected in a target time period, and N is a positive integer greater than 1;
the first determining module is used for determining the traffic state description information corresponding to the road section to be detected in the target time interval according to the N pieces of track information;
and the second determining module is used for determining whether a traffic abnormal event occurs in the road section to be detected in the target time interval according to the traffic state description information.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method according to the first aspect.
The method, the device, the electronic equipment and the storage medium for detecting the traffic abnormal event have the following beneficial effects:
the method comprises the steps of firstly obtaining track information of vehicles appearing in a target time period of a road section to be detected, then determining traffic state information of the road section according to the vehicle track information, and finally determining whether a traffic abnormal event occurs according to the traffic state information. Therefore, the detection and monitoring of the traffic abnormal event are realized.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flow chart of a method of detecting a traffic anomaly event according to a first embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram of a method for detecting traffic anomalies according to a second embodiment of the present disclosure;
fig. 3 is a schematic flow chart of a method of detecting a traffic anomaly event according to a third embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a traffic abnormality event detection device according to a fourth embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a traffic abnormality event detection device according to a fifth embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing a method of detecting a traffic anomaly event according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
With the continuous acceleration of the rhythm of urban life, the requirements of people on traffic conditions during daily travel are higher and higher. However, under the condition that the number of the current social vehicles is increased, abnormal conditions such as traffic jam and traffic accidents occur on roads inevitably. The traffic abnormal event monitoring system has important significance in order to timely recover normal road conditions and detect and monitor traffic abnormal events.
The detection method, apparatus, electronic device, and storage medium of the traffic abnormal event of the present disclosure are described below with reference to the drawings.
Fig. 1 is a flowchart illustrating a method for detecting a traffic abnormal event according to a first embodiment of the present disclosure, where the method may be executed by a device for detecting a traffic abnormal event provided by the present disclosure, and may also be executed by an electronic device provided by the present disclosure, where the electronic device may include, but is not limited to, a terminal device such as a desktop computer, a tablet computer, and the like, and may also be a server. The present disclosure will be explained below by taking as an example a detection apparatus for a traffic anomaly event provided by the present disclosure, and a detection method for a traffic anomaly event provided by the present disclosure.
As shown in fig. 1, the method for detecting a traffic abnormal event may include the following steps:
step 101, acquiring N pieces of track information of N vehicles corresponding to a road section to be detected in a target time period, wherein N is a positive integer greater than 1.
The road section to be detected can be any type of road section. Such as a road segment having an intersection, or an intermediate road segment of any road, etc., which are not limited by this disclosure.
The target period may be a period having an arbitrary duration. Such as 7 to 9 am, or 5 to 8 pm, etc., which are not limited by this disclosure.
In the embodiment of the disclosure, the N pieces of track information of the N vehicles corresponding to the road section to be detected in the target time period may be acquired by using data of different sources and types.
For example, track information of each vehicle in the video is acquired in a labeling or tracking manner according to a monitoring video of the road section to be detected in the target time period.
Or acquiring the track information of each vehicle according to the positioning data of the vehicles appearing on the road section to be detected in the target time period.
It should be noted that the above example is only an example, and cannot be taken as a limitation on acquiring the vehicle track information of the road segment to be detected in the target time period in the embodiment of the present disclosure.
And step 102, determining traffic state description information corresponding to the road section to be detected in the target time period according to the N pieces of track information.
It is understood that the track information of a vehicle can reflect the motion state of the vehicle to some extent.
For example, if the track information of a vehicle does not change for a certain period of time, it may indicate that the vehicle is always in a parking state. Or, the track information of a vehicle changes little within a certain time period, which can indicate that the running speed of the vehicle is slow.
Correspondingly, the track information of a plurality of vehicles can reflect the traffic state of a certain road section to a certain extent.
For example, if the track information of a plurality of vehicles does not change within a certain period of time, it may indicate that the road section may be congested. Or the track information of some vehicles does not change within a certain time period, and the track information of other vehicles changes normally within the same time period, which can indicate that a traffic accident may occur in the road section.
Therefore, in the embodiment of the present disclosure, the traffic state description information corresponding to the road segment to be detected in the target time period may be determined according to the N pieces of track information.
For example, N feature vectors of N pieces of trajectory information may be extracted, respectively, and then the traffic state describing information may be determined based on the N feature vectors.
The N feature vectors are extracted, and any type of neural network model may be adopted, such as a depth residual error network model, a graph convolution neural network model, a Transformen model, and the like, which is not limited in this disclosure.
It should be noted that the above examples are only illustrative, and should not be taken as a limitation on the traffic state description information of the embodiments of the present disclosure.
And 103, determining whether the road section to be detected has a traffic abnormal event in the target time period according to the traffic state description information.
The traffic abnormal event may be any type of abnormal event in a traffic scene, such as a traffic jam, a traffic accident, and the like, which is not limited in this disclosure.
In the embodiment of the disclosure, according to the traffic state description information, it may be determined only whether a traffic abnormal event occurs in the road segment to be detected in the target time period, or the type of the occurring traffic abnormal event may be further determined.
For example, interactive features among the N pieces of track information are determined based on the N feature vectors, the interactive features are classified, and then whether a traffic abnormal event occurs in the road section to be detected in the target time period or not is determined based on the interactive features.
The interactive features among the N pieces of track information are determined based on the N pieces of feature vectors, and a global average pooling layer, a global maximum pooling layer, a concat function, or the like of the convolutional neural network model may be adopted, which is not limited by the present disclosure.
It should be noted that the above example is only an example, and cannot be taken as a limitation for determining whether a traffic abnormal event occurs in a road segment to be detected in a target time period in the embodiment of the present disclosure.
The method for detecting the traffic abnormal event comprises the steps of firstly obtaining track information of vehicles appearing in a target time period of a road section to be detected, then determining traffic state information of the road section according to the track information of the vehicles, and finally determining whether the traffic abnormal event occurs according to the traffic state information. Therefore, the detection and monitoring of the traffic abnormal event are realized.
Fig. 2 is a flowchart illustrating a method for detecting a traffic anomaly event according to a second embodiment of the present disclosure. As shown in fig. 2, the method for detecting a traffic abnormal event may include the following steps:
step 201, acquiring N track information of N vehicles corresponding to the road section to be detected in the target time period. And under the condition that the duration of the target time interval is greater than a threshold value, dividing each track information into a plurality of sub-track information respectively, wherein each sub-track information corresponds to one sub-time interval.
Note that, the target time period may be any time period, such as 30 minutes, 1 hour, and the like. Therefore, a plurality of traffic abnormality events may occur at different times of the target period.
In the embodiment of the disclosure, in order to determine a plurality of abnormal traffic events occurring in a target time period, a time length threshold may be set, and when the time length of the target time period is greater than the threshold, the target time period is divided into a plurality of sub-time periods, and each piece of track information is divided into a plurality of pieces of sub-track information according to the corresponding sub-time periods.
For example, the time threshold is set to be 1 minute, and when the time of the target time interval is 30 minutes, the target time interval is divided into 30 sub-time intervals, and then the track information of each vehicle is divided into 30 sub-track information.
The specific implementation manner of obtaining the vehicle track information may refer to detailed descriptions of other embodiments of the disclosure, which are not repeated herein.
Step 202, determining traffic state description information corresponding to each sub-period according to the N sub-track information corresponding to each sub-period.
Step 203, determining whether a traffic abnormal event occurs in each sub-period according to the corresponding traffic state description information in each sub-period.
The detailed implementation manner of step 202-203 can refer to the detailed description of other embodiments of the present disclosure, and will not be described herein again.
According to the method for detecting the traffic abnormal event, the target time interval is divided according to the duration to obtain N pieces of sub-track information corresponding to a plurality of sub-time intervals respectively. And then respectively determining the traffic state description information corresponding to each sub-period according to the N sub-track information respectively corresponding to the plurality of sub-periods. And finally, determining whether a traffic abnormal event occurs in each sub-period according to the corresponding traffic state description information in each sub-period. Therefore, the detection and monitoring of the traffic abnormal events in different time periods within the target time period are realized, and the accuracy and reliability of the detection of the traffic abnormal events are improved.
Fig. 3 is a flowchart illustrating a method for detecting a traffic anomaly event according to a third embodiment of the present disclosure. As shown in fig. 3, the method for detecting a traffic abnormal event may include the following steps:
step 301, acquiring N track information of N vehicles corresponding to a road section to be detected in a target time period, where N is a positive integer greater than 1.
As can be seen from the description of other embodiments of the present disclosure, the N pieces of track information of the N vehicles corresponding to the road segment to be detected in the target time period may be obtained based on the monitoring video of the road segment to be detected in the target time period, or may be based on the positioning data of the vehicles appearing on the road segment to be detected in the target time period.
The monitoring video of the road section to be detected in the target time period can be obtained by shooting through the road test monitoring equipment or acquired through the vehicle-mounted video equipment. The positioning data of the vehicles appearing on the road section to be detected in the target time period can be acquired by the vehicle positioning system.
It should be noted that, when the traffic flow on the road is large, part of the vehicles may disappear from the video due to the occlusion of other vehicles during the driving process, so that the complete track information of the vehicles in the target time period cannot be obtained.
In addition, the vehicle positioning system may also suffer from signal loss, network delay, etc., which may result in failure to obtain complete positioning data of the vehicle in the target time period.
In a possible implementation manner of the present disclosure, N pieces of track information of N vehicles corresponding to the road section to be detected in the target time period may be determined according to the video data of the road section to be detected in the target time period and the positioning data of each vehicle in the target time period.
For example, track information of each vehicle in a target time period is determined according to video data, when track information of a vehicle is missing in a certain time period, positioning data of the vehicle in the corresponding time period is acquired, missing track information is supplemented according to the positioning data, and then complete track information of the vehicle is acquired.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
In the embodiment of the disclosure, the track information of the vehicles is determined by combining the video data of the road section to be detected in the target time interval and the positioning data of each vehicle in the target time interval, so that the integrity and the accuracy of the track information of the vehicles are fully ensured, and a support is provided for improving the accuracy and the reliability of the detection of the abnormal traffic events.
Step 302, determining traffic state description information corresponding to the road section to be detected in the target time interval according to the N pieces of track information, wherein the traffic state description information includes a duration corresponding to a traffic abnormal state of the road section to be detected in the target time interval.
The N pieces of trajectory information may be respective continuous position coordinates of the N vehicles in the target time period.
In one possible implementation manner of the present disclosure, each track information may include a plurality of data pair sequences, where each data pair includes a coordinate point and a time.
For example, for the vehicle a, the trajectory information may be a { (x)1，y1，t1)，(x2，y2，t2)，(x3，y3，t3)，……，(xn，yn，tn)}. Wherein (x)n，yn) For vehicle A at tnCoordinate point of time, t1, t2……，tnAnd respectively corresponding to the positioning time of each coordinate point.
It is understood that when the position coordinates of a vehicle do not change for a certain period of time, it can indicate that the vehicle is always parked. Alternatively, when the position coordinates of a vehicle change little within a certain period of time, it may indicate that the traveling speed of the vehicle is slow.
In a possible implementation manner of the present disclosure, the congestion status description information corresponding to the road segment to be detected in the target time period may be determined according to the matching degree between the coordinate points in each adjacent data pair in each track information.
For example, for the track information of each vehicle, the difference value of each adjacent coordinate point may be calculated, and the difference value of each adjacent coordinate point may be used as the congestion state description information corresponding to the vehicle.
The difference between adjacent coordinate points may be calculated only by the difference between x coordinates or the difference between y coordinates, or may be calculated by the sum of the square of the difference between x coordinates and the square of the difference between y coordinates, which is not limited in this disclosure.
It can be understood that if the position coordinates of some vehicles do not change in the target time period of the road segment to be detected, and the position coordinates of other vehicles change normally in the same time period, it can be shown that a traffic accident may occur in the road segment.
In a possible implementation manner of the present disclosure, the accident status description information corresponding to the road segment to be detected in the target time period may be determined according to the matching degree between the coordinate points in each piece of track information corresponding to the same time.
For example, the difference between the coordinate points of each piece of trajectory information at the same time is calculated according to the trajectory information of N vehicles, and the difference between the coordinate points at the same time is used as the accident state description information corresponding to the time.
The difference between adjacent coordinate points may be calculated only by the difference between x coordinates or the difference between y coordinates, or may be calculated by the sum of the square of the difference between x coordinates and the square of the difference between y coordinates, which is not limited in this disclosure.
It should be noted that the above examples are only illustrative, and should not be taken as a limitation on the traffic status description information in the embodiments of the present disclosure.
In the embodiment of the disclosure, the congestion state description information corresponding to the road section to be detected in the target time interval is determined according to the matching degree between the coordinate points in each adjacent data pair in each track information, and the accident state description information corresponding to the road section to be detected in the target time interval is determined according to the matching degree between the coordinate points in each track information corresponding to the same time, so that the division of the traffic state description information types is realized, and a support is provided for the division of the traffic abnormal event types.
In the embodiment of the present disclosure, the traffic state may be divided into a normal traffic state and an abnormal traffic state. The abnormal traffic state may be further classified into a traffic jam state, a traffic accident state, and the like according to the type, which is not limited in this disclosure.
In the embodiment of the disclosure, the traffic state description information may include a duration corresponding to a normal traffic state and a duration corresponding to an abnormal traffic state of the road segment to be detected in the target time period. The duration corresponding to the abnormal traffic state may include a duration corresponding to a traffic jam state, a duration corresponding to a traffic accident state, and the like, which is not limited in this disclosure.
For example, the duration corresponding to the traffic congestion state is determined, the difference value of each adjacent coordinate point can be respectively calculated according to the track information of each vehicle, and the difference value of each adjacent coordinate point and the duration corresponding to each difference value are used as the congestion state description information corresponding to the vehicle. And when more than one difference is smaller than the set threshold, calculating the sum of the time lengths corresponding to the differences to serve as the congestion time length of the vehicle. And respectively calculating the congestion time length corresponding to each vehicle, and taking the maximum time length as the time length corresponding to the traffic congestion state.
Or, determining the time length corresponding to the traffic accident state, respectively calculating the difference value of the coordinate points of each piece of track information at the same time according to the track information of the N vehicles, and taking the difference value of each coordinate point at the same time as the accident state description information corresponding to the time. And aiming at any two track information, when more than one difference is smaller than a set threshold, calculating the total duration of the state that the difference is smaller than the threshold as the accident duration of two corresponding vehicles. And calculating the accident duration of any two vehicles, and taking the maximum duration as the duration corresponding to the traffic accident state.
Step 303, determining a traffic anomaly index of the road section to be detected in the target time period according to the time length corresponding to the traffic anomaly state and the time length of the target time period.
In the embodiment of the present disclosure, the abnormal traffic state may include various states such as traffic jam and traffic accident. Therefore, when the traffic anomaly index of the road section to be detected in the target time period is determined according to the time length corresponding to the traffic anomaly state and the time length of the target time period, the traffic anomaly index corresponding to each traffic anomaly state can be respectively determined.
For example, the duration of the target time period is 5 minutes, and if the duration corresponding to the traffic congestion state in the target time period is 4 minutes, the traffic congestion index may be 0.8.
Or, the duration of the target time period is 5 minutes, and if the duration corresponding to the traffic accident state in the target time period is 2 minutes, the traffic accident index may be 0.4.
It should be noted that the above examples are only illustrative, and should not be taken as limitations on the traffic abnormal state and the traffic abnormal index in the embodiments of the present disclosure.
And 304, determining that a traffic abnormal event occurs in the road section to be detected in the target time period under the condition that the traffic abnormal index is within the first numerical range.
And 305, determining that no traffic abnormal event occurs in the road section to be detected in the target time period under the condition that the traffic abnormal index is within the second numerical range.
In the embodiment of the present disclosure, the traffic abnormality index may be divided into a plurality of indexes according to the type of the traffic abnormality, such as a traffic congestion index, a traffic accident index, and the like, which is not limited in the present disclosure.
Correspondingly, whether the abnormal traffic event occurs in the road section to be detected in the target time period or not and the type of the occurred abnormal traffic event can be determined according to the abnormal traffic index.
For example, when the traffic congestion index is less than 0.8, it is determined that no traffic congestion event occurs in the target time period on the road section to be detected. And when the traffic jam index is more than or equal to 0.8, determining that a traffic jam event occurs in the target time period on the road section to be detected.
Or when the traffic accident index is less than 0.5, determining that no traffic accident event occurs in the target time interval on the road section to be detected. And when the traffic accident index is more than or equal to 0.5, determining that a traffic accident event occurs in the road section to be detected in the target time period.
It should be noted that, in the embodiment of the present disclosure, for different types of traffic abnormal events, the corresponding traffic abnormal indexes may be set in the same numerical range, or may be set in different numerical ranges, which is not limited in the present disclosure.
It is understood that, in the embodiment of the present disclosure, the steps 304 and 305 are alternatively performed according to the numerical range of the traffic abnormality index.
The method for detecting the traffic abnormal event comprises the steps of firstly determining different types of traffic state description information according to vehicle track information; then determining different types of traffic abnormal indexes according to different types of traffic state description information; and finally, determining whether the road section to be detected has the traffic abnormal event in the target time period and the type of the traffic abnormal event according to the traffic abnormal indexes of different types. Therefore, the accuracy of detecting the traffic abnormal events is further improved, and a guarantee is provided for taking corresponding counter measures according to the types of the traffic abnormal events subsequently.
According to the embodiment of the disclosure, the disclosure further provides a device for detecting the traffic abnormal event.
Fig. 4 is a schematic structural diagram of a traffic abnormal event detection device according to a fourth embodiment of the present disclosure. As shown in fig. 4, the traffic abnormality event detection device 40 includes: a first acquisition module 410, a first determination module 420, and a second determination module 430.
The first obtaining module 410 is configured to obtain N pieces of track information of N vehicles corresponding to a road segment to be detected in a target time period, where N is a positive integer greater than 1.
The first determining module 420 is configured to determine traffic state description information corresponding to the road segment to be detected in the target time period according to the N pieces of track information.
The second determining module 430 is configured to determine whether a traffic abnormal event occurs in the road segment to be detected in the target time period according to the traffic state description information.
In a possible implementation manner of the embodiment of the present disclosure, the first obtaining module 410 is specifically configured to:
acquiring N track information of N vehicles corresponding to the road section to be detected in the target time period, and
and under the condition that the duration of the target time interval is greater than a threshold value, dividing each track information into a plurality of sub-track information respectively, wherein each sub-track information corresponds to one sub-time interval.
The first determining module 420 is specifically configured to: and determining the traffic state description information corresponding to each sub-period according to the N sub-track information corresponding to each sub-period.
The second determining module 430 is specifically configured to: and determining whether the traffic abnormal event occurs in each sub-period according to the corresponding traffic state description information in each sub-period.
Fig. 5 is a schematic structural diagram of a traffic abnormal event detection device according to a fifth embodiment of the present disclosure. As shown in fig. 5, the traffic abnormality event detection device 40 includes: a first acquisition module 410, a first determination module 420, and a second determination module 430.
The first obtaining module 410 is configured to determine, according to the video data of the road segment to be detected in the target time period and the positioning data of each vehicle in the target time period, N pieces of track information of N vehicles corresponding to the road segment to be detected in the target time period.
In one possible implementation manner of the present disclosure, each track information includes a plurality of data pair sequences, where each data pair includes a coordinate point and a time.
A first determining module 420 comprising:
the fourth determining unit 421 is configured to determine congestion status description information corresponding to the road segment to be detected in the target time period according to the matching degree between the coordinate points in each adjacent data pair in each track information.
And/or the presence of a gas in the gas,
the fifth determining unit 422 is configured to determine accident status description information corresponding to the road segment to be detected in the target time period according to the matching degree between the coordinate points in each piece of track information corresponding to the same time.
In a possible implementation manner of the present disclosure, the traffic state description information includes a duration corresponding to a normal traffic state and a duration corresponding to an abnormal traffic state of the road segment to be detected in the target time period.
A second determining module 430, comprising:
the first determining unit 431 is configured to determine a traffic anomaly index of the road segment to be detected in the target time period according to the time length corresponding to the normal traffic state and the time length corresponding to the abnormal traffic state.
The second determining unit 432 is configured to determine that a traffic abnormal event occurs in the to-be-detected road segment within the target time period when the traffic abnormal index is within the first numerical range.
The third determining unit 433 is configured to determine that no traffic abnormal event occurs in the road segment to be detected in the target time period when the traffic abnormal index is within the second numerical value range.
It should be noted that the foregoing explanation of the embodiment of the method for detecting a traffic abnormal event is also applicable to the apparatus for detecting a traffic abnormal event of the embodiment, and the implementation principle thereof is similar and will not be described herein again.
The detection device for the traffic abnormal event of the embodiment of the disclosure firstly obtains the track information of the vehicle appearing in the target time interval of the road section to be detected, then determines the traffic state information of the road section according to the vehicle track information, and finally determines whether the traffic abnormal event occurs according to the traffic state information. Therefore, the detection and monitoring of the traffic abnormal event are realized.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the detection method of a traffic abnormal event. For example, in some embodiments, the method of detecting traffic anomalies may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the above described method of detecting traffic anomalies may be performed. Alternatively, in other embodiments, the calculation unit 601 may be configured by any other suitable means (e.g. by means of firmware) to perform the detection method of the traffic anomaly event.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims (13)

1. A method of detecting a traffic anomaly event, comprising:
acquiring N pieces of track information of N vehicles corresponding to a road section to be detected in a target time period, wherein N is a positive integer greater than 1;
determining traffic state description information corresponding to the road section to be detected in the target time period according to the N pieces of track information;
and determining whether the road section to be detected has a traffic abnormal event in the target time period or not according to the traffic state description information.
2. The method according to claim 1, wherein the acquiring of N track information of N vehicles corresponding to the road segment to be detected in the target time period includes:
under the condition that the duration of the target time interval is greater than a threshold value, dividing each track information into a plurality of sub-track information respectively, wherein each sub-track information corresponds to one sub-time interval;
determining traffic state description information corresponding to the road section to be detected in the target time period according to the N pieces of track information, wherein the determining comprises the following steps:
determining traffic state description information corresponding to each sub-period according to N pieces of sub-track information corresponding to each sub-period;
the determining whether the road section to be detected has the abnormal traffic event in the target time period according to the traffic state description information includes:
and determining whether a traffic abnormal event occurs in each sub-period according to the corresponding traffic state description information in each sub-period.
3. The method of claim 1, wherein the traffic state description information includes a duration corresponding to a traffic abnormal state of the to-be-detected road segment in the target time period, and the determining whether a traffic abnormal event occurs in the to-be-detected road segment in the target time period according to the traffic state description information includes:
determining a traffic abnormality index of the road section to be detected in the target time period according to the time length corresponding to the traffic abnormality state and the time length of the target time period;
determining that a traffic abnormal event occurs in the road section to be detected in the target time period under the condition that the traffic abnormal index is within a first numerical range;
and under the condition that the traffic abnormality index is within a second numerical value range, determining that no traffic abnormality event occurs in the road section to be detected in the target time period.
4. The method according to claim 1, wherein the acquiring of N track information of N vehicles corresponding to the road segment to be detected in the target time period includes:
and determining N pieces of track information of N vehicles corresponding to the road section to be detected in the target time period according to the video data of the road section to be detected in the target time period and the positioning data of each vehicle in the target time period.
5. The method according to any one of claims 1 to 4, wherein each of the track information includes a plurality of data pair sequences, each of the data pairs includes a coordinate point and a time, and determining the traffic state description information corresponding to the road segment to be detected in the target time period according to the N pieces of track information includes:
determining congestion state description information corresponding to the road section to be detected in the target time period according to the matching degree between coordinate points in each adjacent data pair in each track information;
and/or the presence of a gas in the gas,
and determining accident state description information corresponding to the road section to be detected in the target time period according to the matching degree between the coordinate points in each piece of track information corresponding to the same moment.
6. A traffic anomaly event detection device, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring N track information of N vehicles corresponding to a road section to be detected in a target time period, and N is a positive integer greater than 1;
the first determining module is used for determining the traffic state description information corresponding to the road section to be detected in the target time interval according to the N pieces of track information;
and the second determining module is used for determining whether a traffic abnormal event occurs in the road section to be detected in the target time interval according to the traffic state description information.
7. The device of claim 6, wherein the first obtaining module is configured to obtain N pieces of track information of N vehicles corresponding to the road segment to be detected in the target time period;
and the number of the first and second groups,
under the condition that the duration of the target time interval is greater than a threshold value, dividing each track information into a plurality of sub-track information respectively, wherein each sub-track information corresponds to one sub-time interval;
the first determining module is configured to determine traffic state description information corresponding to each sub-period according to N pieces of sub-trajectory information corresponding to each sub-period;
the second determining module is used for determining whether a traffic abnormal event occurs in each sub-period according to the corresponding traffic state description information in each sub-period.
8. The device of claim 6, wherein the traffic state description information includes a duration corresponding to a traffic abnormal state of the road segment to be detected in the target time period;
the second determining module includes:
the first determining unit is used for determining a traffic abnormality index of the road section to be detected in the target time period according to the time length corresponding to the traffic abnormality state and the time length of the target time period;
the second determining unit is used for determining that a traffic abnormal event occurs in the road section to be detected in the target time period under the condition that the traffic abnormal index is located in a first numerical range;
and the third determining unit is used for determining that no traffic abnormal event occurs in the road section to be detected in the target time period under the condition that the traffic abnormal index is within a second numerical value range.
9. The device according to claim 6, wherein the first obtaining module is configured to determine, according to the video data of the to-be-detected road segment in the target time period and the positioning data of each vehicle in the target time period, N pieces of track information of N vehicles corresponding to the to-be-detected road segment in the target time period.
10. The apparatus according to any one of claims 6-9, wherein each of the trajectory information includes a plurality of data pair sequences, wherein each data pair includes a coordinate point and a time;
the first determining module includes:
a fourth determining unit, configured to determine congestion state description information corresponding to the road segment to be detected in the target time period according to a matching degree between coordinate points in each adjacent data pair in each piece of track information;
and/or the presence of a gas in the gas,
and the fifth determining unit is used for determining the accident state description information corresponding to the road section to be detected in the target time interval according to the matching degree between the coordinate points in each piece of track information corresponding to the same time.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.
CN202110722070.8A 2021-06-28 2021-06-28 Method and device for detecting traffic abnormal event, electronic equipment and storage medium Active CN113593218B (en)

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