CN113269042A - Intelligent traffic management method and system based on running vehicle violation identification - Google Patents
Intelligent traffic management method and system based on running vehicle violation identification Download PDFInfo
- Publication number
- CN113269042A CN113269042A CN202110450498.1A CN202110450498A CN113269042A CN 113269042 A CN113269042 A CN 113269042A CN 202110450498 A CN202110450498 A CN 202110450498A CN 113269042 A CN113269042 A CN 113269042A
- Authority
- CN
- China
- Prior art keywords
- violation
- vehicle
- abnormal
- image
- traffic management
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007726 management method Methods 0.000 title claims abstract description 52
- 230000002159 abnormal effect Effects 0.000 claims abstract description 63
- 238000012544 monitoring process Methods 0.000 claims abstract description 35
- 238000000034 method Methods 0.000 claims abstract description 21
- 230000006399 behavior Effects 0.000 claims description 98
- 238000004458 analytical method Methods 0.000 claims description 28
- 238000004891 communication Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 4
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 206010000117 Abnormal behaviour Diseases 0.000 description 8
- 238000010191 image analysis Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000003825 pressing Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Multimedia (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses an intelligent traffic management method and system based on violation identification of running vehicles, wherein the method comprises the following steps: obtaining each vehicle in a monitoring video of a current monitoring point; determining a vehicle with abnormal running track, and recording as a vehicle to be determined against the regulations; obtaining the geographical position before the starting time of the abnormal running track of the vehicle to be determined; and judging whether the running track of the violation vehicle to be determined after the geographical position is violated according to whether the running track of other vehicles after passing through the geographical position is abnormal. The invention judges whether the violation vehicle to be determined violates the regulations by judging whether the running tracks of the violation vehicle to be determined and other vehicles at the same geographical position of the violation vehicle are abnormal, thereby improving the accuracy of judging the violation vehicle.
Description
Technical Field
The invention relates to the technical field of traffic management, in particular to an intelligent traffic management method and system based on violation identification of running vehicles.
Background
In the prior art, the detection and management of vehicle violation events in traffic management are generally implemented by automatically detecting the violation and determining a driver through image analysis, however, in the traffic management process, the anomalies of traffic environment and traffic facility equipment are various and may not be timely operated and maintained, so that some errors can be caused in the violation judgment of the vehicle running track anomaly only through image acquisition and image analysis.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent traffic management method and system based on the violation identification of a running vehicle, so as to improve the accuracy of judging the violation behaviors of the vehicle in the traffic management process.
In a first aspect, the present application provides an intelligent traffic management method based on vehicle violation identification, comprising:
obtaining each vehicle in a monitoring video of a current monitoring point;
determining a vehicle with abnormal running track, and recording as a vehicle to be determined against the regulations;
obtaining the geographical position before the starting time of the abnormal running track of the vehicle to be determined;
judging whether the running track of the vehicle to be determined violating the regulations after the geographical position is violated according to whether the running track of other vehicles after passing the geographical position is abnormal;
and when the vehicle judges that the violation occurs, acquiring the violation state image of the vehicle, the violation driver, confirming the image of the violation driver and the violation punishment object information for associated storage.
In some possible embodiments, based on the first aspect, the determining a vehicle with a travel track abnormality includes:
inputting a monitoring video image into a preset multi-violation behavior probability distribution analysis model to obtain the prediction probability of at least one violation behavior of the vehicle;
taking the vehicle violation behavior image as a first violation image set, and acquiring target associated data based on the first violation image set;
and determining the violation behaviors of the vehicle according to the target associated data and at least one violation behavior analysis model, wherein the target associated judging data are image characteristic data which are preset by each violation behavior analysis model and are used for analyzing one violation behavior.
Based on the first aspect, in some possible embodiments, the regarding the vehicle violation behavior image as the first violation image set includes:
a predicted probability of at least one violation of the vehicle based on the multi-violation probability distribution analysis model;
sequencing the probability of each driving behavior from large to small;
if the probability that the vehicle driving behavior belongs to the normal state is the maximum and is greater than a first preset threshold value, not extracting the video image corresponding to the driving track of the vehicle;
otherwise, extracting a video image corresponding to the driving track of the vehicle as a first violation image set.
Based on the first aspect, in some possible embodiments, the determining the violation of the vehicle according to the target associated data and the at least one violation analysis model includes:
and judging whether the target association judgment data is abnormal or not through a preset judgment rule facing different types of violation behaviors based on the target association judgment data, and determining the violation behaviors corresponding to the target association judgment data for the abnormal data.
Based on the first aspect, in some possible embodiments, target association decision data adopted by different violation behavior analysis models are different, and the method for obtaining the target association decision data includes:
extracting general features including a positioning area of the vehicle in the image based on the monitoring video image;
and acquiring the unique characteristics corresponding to the abnormal driving behavior type based on the monitoring video image.
Based on the first aspect, in some possible embodiments, the extracting general features based on the surveillance video image is performed by performing pixel processing using a convolutional neural network to obtain pixel-level features, the obtaining specific features corresponding to the abnormal driving behavior category based on the surveillance video image is performed by analyzing a second preset number of image frames based on the pixel-level features of each image frame to obtain the specific features corresponding to the abnormal driving behavior category.
Based on the first aspect, in some possible embodiments, the judging whether the running track of the violation vehicle to be determined after the geographical location is violated according to whether the running track of the other vehicle after passing the geographical location is abnormal includes:
and in a period of continuous time before and after the vehicle passes through the geographic position, if the violation behaviors of all other vehicles passing through the geographic position are consistent with the vehicle, judging that the violation behaviors do not occur in the running track of the geographic position of the vehicle.
In a second aspect, the present application provides an intelligent traffic management system based on identification of violations of moving vehicles, comprising:
the video image unit is used for acquiring each vehicle in the monitoring video of the current monitoring point;
the device comprises a to-be-determined violation vehicle acquisition unit, a to-be-determined violation vehicle acquisition unit and a to-be-determined violation vehicle acquisition unit, wherein the to-be-determined violation vehicle acquisition unit is used for determining a vehicle with abnormal running track and recording the vehicle as the to-be-determined violation vehicle;
the abnormal track information acquisition unit is used for acquiring the geographical position before the starting time of the abnormal running track of the violation vehicle to be determined;
the violation vehicle acquisition unit is used for judging whether the running track of the violation vehicle to be determined after the geographical position is violated according to whether the running track of other vehicles after passing through the geographical position is abnormal;
and the violation information storage unit is used for obtaining the violation state image of the vehicle, the violation driver, the image of the violation driver and the violation punishment object information for associated storage when the vehicle judges that the violation occurs.
In a third aspect, the present application provides an intelligent traffic management device, comprising: a processor and a communication interface;
the communication interface is coupled with the processor, and the processor acquires a monitoring video image of a current monitoring point through the communication interface;
the processor is configured to support the intelligent traffic management device to implement the functions of the first aspect described above or any one of the possible implementations of the first aspect.
Based on the third aspect, in some possible embodiments, the intelligent traffic management device further includes: the memory is used for storing computer execution instructions and data necessary for the intelligent traffic management equipment; when the intelligent traffic management device runs, the processor executes the computer execution instructions stored in the memory to enable the intelligent traffic management device to execute the intelligent traffic management method.
The intelligent traffic management method and system based on the violation identification of the running vehicles have the following beneficial effects:
1. the vehicle with abnormal running track is determined to be the violation vehicle to be determined based on the image analysis, and then whether the violation vehicle to be determined violates the regulations is determined by determining whether the running track of other vehicles is abnormal in the same geographical position of the violation vehicle, so that the accuracy of determining the violation vehicle is improved, and the misjudgment based on the image acquisition and the image analysis is avoided.
2. The method comprises the steps of inputting a monitoring video image into a preset multi-violation behavior probability distribution analysis model to obtain the prediction probability of at least one violation behavior of the vehicle, and determining whether the violation behavior of the vehicle actually occurs according to the prediction probability and the corresponding violation behavior analysis model, so that the accurate judgment of various possible violation behaviors is realized, and the accuracy of image analysis of the violation behaviors is improved.
Drawings
FIG. 1 is a flow chart of an intelligent traffic management method based on vehicle violation identification in an embodiment of the present invention;
FIG. 2 is a flow chart of a method for analyzing vehicle violation behavior in an embodiment of the present invention;
fig. 3 is a block diagram of an intelligent traffic management system based on the identification of a violation of a moving vehicle in an embodiment of the present invention.
Detailed Description
The embodiments of the present application will be described below with reference to the drawings. In the following description, reference is made to the accompanying drawings which form a part hereof and in which is shown by way of illustration specific aspects of embodiments of the present application or in which specific aspects of embodiments of the present application may be employed. It should be understood that embodiments of the present application may be used in other ways and may include structural or logical changes not depicted in the drawings. The following detailed description is, therefore, not to be taken in a limiting sense. For example, it should be understood that the disclosure in connection with the described methods may equally apply to the corresponding apparatus or system for performing the methods, and vice versa. For example, if one or more particular method steps are described, the corresponding apparatus may comprise one or more units, such as functional units, to perform the described one or more method steps (e.g., a unit performs one or more steps, or multiple units, each of which performs one or more of the multiple steps), even if such one or more units are not explicitly described or illustrated in the figures. On the other hand, for example, if a particular apparatus is described based on one or more units, such as functional units, the corresponding method may comprise one step to perform the functionality of the one or more units (e.g., one step performs the functionality of the one or more units, or multiple steps, each of which performs the functionality of one or more of the plurality of units), even if such one or more steps are not explicitly described or illustrated in the figures. Further, it is to be understood that features of the various exemplary embodiments and/or aspects described herein may be combined with each other, unless explicitly stated otherwise.
The intelligent traffic management method based on the violation identification of the running vehicle comprises the following steps:
(1) obtaining each vehicle in a monitoring video of a current monitoring point;
(2) determining a vehicle with abnormal running track, and recording as a vehicle to be determined against the regulations;
(3) obtaining the geographical position before the starting time of the abnormal running track of the vehicle to be determined;
(4) judging whether the running track of the vehicle to be determined violating the regulations after the geographical position is violated according to whether the running track of other vehicles after passing the geographical position is abnormal;
specifically, the step of judging whether the running track of the violation vehicle to be determined after the geographical position is violated according to whether the running track of other vehicles after passing the geographical position is abnormal or not includes:
and in a period of continuous time before and after the vehicle passes through the geographic position, if the violation behaviors of all other vehicles passing through the geographic position are consistent with the vehicle, judging that the violation behaviors do not occur in the running track of the geographic position of the vehicle.
(5) And when the vehicle judges that the violation occurs, acquiring the violation state image of the vehicle, the violation driver, confirming the image of the violation driver and the violation punishment object information for associated storage.
In the embodiment of the invention, a vehicle with abnormal running track is determined, the vehicle with abnormal running track can be a vehicle which is judged to have violation behaviors based on video image analysis, the vehicle is determined to be a vehicle which is possible to have violation behaviors, then, whether the running tracks of other vehicles at the same geographical position are abnormal or not is determined by the geographical position of the vehicle where the violation behaviors occur, whether the violation vehicles are violated or not is determined, if the running tracks of other vehicles at the same geographical position are abnormal, specifically, the running tracks of a certain number of other vehicles at the same geographical position are all abnormal, the reason that the running abnormality of the violation vehicles is determined to be possible: the abnormal lane road surface of the current monitoring point causes the abnormal running track of the lane vehicle, or the errors of the acquisition parameters (such as attribute information of an image acquired by acquisition equipment) and analysis algorithm parameters and the like of the current monitoring point for analyzing the violation behaviors cause the analysis errors of the running track, specifically, the inaccurate identification of the lane line is caused by the unclear or partial missing of the lane line in the acquired image, and further the error is caused in the judgment of the abnormal running tracks of the vehicle line pressing and the like. And if the running tracks of other vehicles in the same geographical position are normal, determining that the violation vehicle to be determined is a violation vehicle, and performing multi-dimensional information storage on violation behavior associated data of the vehicle, including violation behavior evidence, information of a driver violating the vehicle and violation punishment objects, so as to perform big data analysis on traffic violation information at a later stage, and the like.
In the step (2), the determining the vehicle with the abnormal traveling track includes:
(21) inputting a monitoring video image into a preset multi-violation behavior probability distribution analysis model to obtain the prediction probability of at least one violation behavior of the vehicle;
in the step, the vehicle running characteristics in the image are extracted based on the monitoring video, the probability that the vehicle running process belongs to a plurality of running behaviors is judged based on the running characteristics, the vehicle running behaviors comprise normal running tracks and abnormal running tracks, the abnormal running tracks can comprise various vehicle illegal running behaviors such as overspeed, red light running, lane change of solid lines, solid line pressing and the like, and the probability of analyzing each possible running behavior can be realized through a probability neural network so as to obtain the occurrence probability of the various running behaviors including normal running tracks and each illegal running behavior.
(22) Taking the vehicle violation behavior image as a first violation image set, and acquiring target associated data based on the first violation image set;
(23) and determining the violation behaviors of the vehicle according to the target associated data and at least one violation behavior analysis model, wherein the target associated judging data are image characteristic data which are preset by each violation behavior analysis model and are used for analyzing one violation behavior.
Each violation behavior analysis model is used for specifically analyzing and judging whether a violation behavior occurs or not, and analyzing whether the violation behavior occurs or not according to input target associated data, wherein the target associated data are characteristic data which are extracted based on the first violation image set and used for analyzing whether the violation behavior occurs or not.
In the embodiment of the invention, probability distribution prediction is carried out on various possible driving behaviors of the vehicle, the current most possible driving behavior of the vehicle is judged according to the probability, and further accurate analysis is carried out on the basis of the most possible driving behavior and target associated data to obtain the determined violation behavior type of the vehicle.
The above-mentioned vehicle violation action image as first violating image set includes:
a predicted probability of at least one violation of the vehicle based on the multi-violation probability distribution analysis model;
sequencing the probability of each driving behavior from large to small;
if the probability that the vehicle driving behavior belongs to the normal state is the maximum and is greater than a first preset threshold value, not extracting the video image corresponding to the driving track of the vehicle;
otherwise, extracting a video image corresponding to the driving track of the vehicle as a first violation image set.
In the embodiment, when the probability that the vehicle driving behavior belongs to the normal is the maximum and is greater than a first preset threshold, the vehicle is judged not to have the violation driving behavior, otherwise, the vehicle is subjected to specific abnormal driving behavior analysis based on the extracted first violation image set, specifically, the vehicle is judged from large to small according to the prediction probability of each abnormal driving behavior category, assuming that the abnormal driving behavior is the first abnormal behavior, the second abnormal behavior and the third abnormal behavior in sequence from large to small according to the occurrence probability, wherein the corresponding occurrence probabilities are the first probability, the second probability and the third probability respectively, the first abnormal behavior category corresponding to the first probability value is analyzed based on the first violation image set, if the first abnormal behavior is determined to occur based on the first violation image set, the vehicle is determined to be the first abnormal behavior, otherwise, the second abnormal behavior category corresponding to the second probability value is analyzed based on the first violation image set, and repeating the abnormal behavior type determining process to obtain the violation behavior type of the vehicle.
The step of determining the violation behaviors of the vehicle according to the target associated data and the at least one violation behavior analysis model is to determine whether a specific violation behavior occurs, and comprises the following steps:
and judging whether the target association judgment data is abnormal or not through a preset judgment rule facing different types of violation behaviors based on the target association judgment data, and determining the violation behaviors corresponding to the target association judgment data for the abnormal data.
The target related judgment data is extracted and obtained based on the running track video of the vehicle, the target related judgment data of different abnormal running behaviors are different, for example, when the abnormal running behavior is overspeed running, positioning, displacement, time characteristic data and the like in the running process of the vehicle are extracted based on the monitoring video image, and when the abnormal running behavior is line pressing, edge lines, lane line characteristics and the like of the target vehicle in the running process of the vehicle are extracted.
The above-mentioned different kinds of violation act-oriented rules are explained by taking the violation act-oriented rule of running red light as an example, and the violation act-oriented rule may be:
obtaining stop lines of all lanes of a traffic intersection;
acquiring a red light time period in each driving direction;
acquiring lane stop line position monitoring video data in a red light time period in a driving direction;
extracting a sequence of image frames based on the surveillance video;
obtaining vehicles positioned in the stop line on the current image frame image for the current image frame, and recording as a first vehicle set;
acquiring vehicles positioned in the stop line on the next image frame, and recording as a second vehicle set;
and comparing the first vehicle set with the second vehicle set, and acquiring the vehicles which exist in the first vehicle set and do not exist in the second vehicle set as the red light violation vehicles, namely the vehicles are subjected to the red light violation behaviors.
Further, in order to accurately analyze whether different violation behaviors occur, target association judgment data adopted by different violation behavior analysis models are different, each violation behavior analysis model adopts characteristic data matched with the corresponding violation behavior as target association judgment data, and the method for acquiring the target association judgment data comprises the following steps:
extracting general features including a positioning area of the vehicle in the image based on the monitoring video image;
and acquiring the unique characteristics corresponding to the abnormal driving behavior type based on the monitoring video image. Wherein extract general characteristic based on the surveillance video image, adopt the convolutional neural network to carry out pixel processing and obtain pixel level characteristic, obtain the peculiar characteristic that corresponds with the unusual behavior category of traveling based on the surveillance video image, include: and analyzing a second preset number of image frames based on the pixel level characteristics of each image frame to acquire the special characteristics corresponding to the abnormal driving behavior category.
In the embodiment of the invention, the first step can be that the training of detecting, identifying and tracking and positioning the vehicle target is carried out through a convolution neural network, the vehicle target can be accurately found in the image through the analysis of the neural network at the pixel level, the pixel points of the non-vehicle target, including the shadow and other background pixel points of the vehicle in the image, can be removed through the pixel value distribution analysis of a single frame image, and the target of the adjacent image frame of the continuous image of the target vehicle can be positioned through the change of the corresponding pixel values of the continuous multi-frame image; and secondly, based on the position area of the vehicle of the continuous image frames, macro violation characteristic data of the vehicle can be further obtained, the macro violation characteristic data comprise characteristics used for judging whether the vehicle has violation driving behaviors such as red light running, lane changing with solid lines and the like, such as displacement change, accurate contour lines and the like of the vehicle, and different specific characteristics are analyzed based on corresponding judgment rules to realize the judgment of different abnormal driving behavior types.
Based on the same inventive concept as the above method, an embodiment of the present invention provides an intelligent traffic management system based on violation identification of a traveling vehicle, where the intelligent traffic management system may implement the intelligent traffic management method in each of the above embodiments, the intelligent traffic management system may be a functional module for implementing the method in each of the above embodiments, the functional module may be implemented by executing corresponding software through hardware, and the hardware or software includes one or more modules corresponding to the above functions, for example, in one possible implementation, the intelligent traffic management system based on violation identification of a traveling vehicle in an embodiment of the present invention includes:
the video image unit is used for acquiring each vehicle in the monitoring video of the current monitoring point;
the device comprises a to-be-determined violation vehicle acquisition unit, a to-be-determined violation vehicle acquisition unit and a to-be-determined violation vehicle acquisition unit, wherein the to-be-determined violation vehicle acquisition unit is used for determining a vehicle with abnormal running track and recording the vehicle as the to-be-determined violation vehicle;
the abnormal track information acquisition unit is used for acquiring the geographical position before the starting time of the abnormal running track of the violation vehicle to be determined;
the violation vehicle acquisition unit is used for judging whether the running track of the violation vehicle to be determined after the geographical position is violated according to whether the running track of other vehicles after passing through the geographical position is abnormal;
and the violation information storage unit is used for obtaining the violation state image of the vehicle, the violation driver, the image of the violation driver and the violation punishment object information for associated storage when the vehicle judges that the violation occurs.
Based on the same inventive concept as the method, an embodiment of the present invention provides an intelligent traffic management device, including: a processor and a communication interface;
the communication interface is coupled with the processor, and the processor acquires a monitoring video image of a current monitoring point through the communication interface;
the processor is used for supporting the intelligent traffic management equipment to realize the intelligent traffic management method.
In some possible embodiments, the intelligent traffic management device further includes: the memory is used for storing computer execution instructions and data necessary for the intelligent traffic management equipment; when the intelligent traffic management device runs, the processor executes the computer execution instructions stored in the memory to enable the intelligent traffic management device to execute the intelligent traffic management method.
Those of skill in the art will appreciate that the functions described in connection with the various illustrative logical blocks, modules, and algorithm steps described in the disclosure herein may be implemented as hardware, software, firmware, or any combination thereof. If implemented in software, the functions described in the various illustrative logical blocks, modules, and steps may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a computer-readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or any communication medium including a medium that facilitates transfer of a computer program from one place to another (e.g., according to a communication protocol). In this manner, a computer-readable medium may generally correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium, such as a signal or carrier wave. A data storage medium may be any available medium that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementing the techniques described herein. The computer program product may include a computer-readable medium.
By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium.
The present invention is not limited to the above-described embodiments, and those skilled in the art will be able to make various modifications without creative efforts from the above-described conception, and fall within the scope of the present invention.
Claims (10)
1. The intelligent traffic management method based on the violation identification of the running vehicle is characterized by comprising the following steps:
obtaining each vehicle in a monitoring video of a current monitoring point;
determining a vehicle with abnormal running track, and recording as a vehicle to be determined against the regulations;
obtaining the geographical position before the starting time of the abnormal running track of the vehicle to be determined;
judging whether the running track of the vehicle to be determined violating the regulations after the geographical position is violated according to whether the running track of other vehicles after passing the geographical position is abnormal;
and when the vehicle judges that the violation occurs, acquiring the violation state image of the vehicle, the violation driver, confirming the image of the violation driver and the violation punishment object information for associated storage.
2. The intelligent traffic management method based on the violation identification of the traveling vehicle as recited in claim 1, wherein the determining the vehicle with the abnormal traveling track comprises the following steps:
inputting a monitoring video image into a preset multi-violation behavior probability distribution analysis model to obtain the prediction probability of at least one violation behavior of the vehicle;
taking the vehicle violation behavior image as a first violation image set, and acquiring target associated data based on the first violation image set;
and determining the violation behaviors of the vehicle according to the target associated data and at least one violation behavior analysis model, wherein the target associated judging data are image characteristic data which are preset by each violation behavior analysis model and are used for analyzing one violation behavior.
3. The intelligent traffic management method based on vehicle violation identification according to claim 2 wherein said taking the vehicle violation image as a first violation image set comprises:
a predicted probability of at least one violation of the vehicle based on the multi-violation probability distribution analysis model;
sequencing the probability of each driving behavior from large to small;
if the probability that the vehicle driving behavior belongs to the normal state is the maximum and is greater than a first preset threshold value, not extracting the video image corresponding to the driving track of the vehicle;
otherwise, extracting a video image corresponding to the driving track of the vehicle as a first violation image set.
4. The intelligent traffic management method based on the identification of driving vehicle violations according to claim 2, wherein the determining of vehicle violations from the target associated data and the at least one violation analysis model, comprises:
and judging whether the target association judgment data is abnormal or not through a preset judgment rule facing different types of violation behaviors based on the target association judgment data, and determining the violation behaviors corresponding to the target association judgment data for the abnormal data.
5. The intelligent traffic management method based on the violation identification of the traveling vehicle according to claim 4, wherein target associated decision data adopted by different violation behavior analysis models are different, and the method for acquiring the target associated decision data comprises the following steps:
extracting general features including a positioning area of the vehicle in the image based on the monitoring video image;
and acquiring the unique characteristics corresponding to the abnormal driving behavior type based on the monitoring video image.
6. The intelligent traffic management method based on the moving vehicle violation identification according to claim 5, wherein the general features are extracted based on the surveillance video image, the pixel processing is performed by adopting a convolutional neural network to obtain the pixel-level features, the unique features corresponding to the abnormal moving behavior category are obtained based on the surveillance video image, and the unique features corresponding to the abnormal moving behavior category are obtained by analyzing a second preset number of image frames based on the pixel-level features of each image frame.
7. The intelligent traffic management method based on the identification of the violation of the driving vehicle as set forth in claim 1, wherein: the step of judging whether the running track of the vehicle to be determined violating the regulations after the geographical position is violated according to whether the running track of other vehicles after passing the geographical position is abnormal or not comprises the following steps:
and in a period of continuous time before and after the vehicle passes through the geographic position, if the violation behaviors of all other vehicles passing through the geographic position are consistent with the vehicle, judging that the violation behaviors do not occur in the running track of the geographic position of the vehicle.
8. Intelligent traffic management system based on vehicle violating regulations discernment that traveles, its characterized in that includes:
the video image unit is used for acquiring each vehicle in the monitoring video of the current monitoring point;
the device comprises a to-be-determined violation vehicle acquisition unit, a to-be-determined violation vehicle acquisition unit and a to-be-determined violation vehicle acquisition unit, wherein the to-be-determined violation vehicle acquisition unit is used for determining a vehicle with abnormal running track and recording the vehicle as the to-be-determined violation vehicle;
the abnormal track information acquisition unit is used for acquiring the geographical position before the starting time of the abnormal running track of the violation vehicle to be determined;
the violation vehicle acquisition unit is used for judging whether the running track of the violation vehicle to be determined after the geographical position is violated according to whether the running track of other vehicles after passing through the geographical position is abnormal;
and the violation information storage unit is used for obtaining the violation state image of the vehicle, the violation driver, the image of the violation driver and the violation punishment object information for associated storage when the vehicle judges that the violation occurs.
9. An intelligent traffic management device, comprising: a processor and a communication interface;
the communication interface is coupled with the processor, and the processor acquires a monitoring video image of a current monitoring point through the communication interface;
the processor is used for supporting the intelligent traffic management device to realize the intelligent traffic management method of any one of the preceding claims 1 to 7.
10. The apparatus of claim 9, wherein the intelligent traffic management apparatus further comprises: the memory is used for storing computer execution instructions and data necessary for the intelligent traffic management equipment; when the intelligent transportation management device is running, the processor executes the computer-executable instructions stored by the memory to cause the intelligent transportation management device to perform the intelligent transportation management method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110450498.1A CN113269042B (en) | 2021-04-25 | 2021-04-25 | Intelligent traffic management method and system based on driving vehicle violation identification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110450498.1A CN113269042B (en) | 2021-04-25 | 2021-04-25 | Intelligent traffic management method and system based on driving vehicle violation identification |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113269042A true CN113269042A (en) | 2021-08-17 |
| CN113269042B CN113269042B (en) | 2024-03-29 |
Family
ID=77229298
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110450498.1A Active CN113269042B (en) | 2021-04-25 | 2021-04-25 | Intelligent traffic management method and system based on driving vehicle violation identification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113269042B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114255588A (en) * | 2021-11-29 | 2022-03-29 | 安徽百诚慧通科技有限公司 | Regional motor vehicle illegal parking prediction method, storage medium and device |
| CN115114312A (en) * | 2022-07-15 | 2022-09-27 | 北京百度网讯科技有限公司 | Map data updating method and device and electronic equipment |
| CN117494029A (en) * | 2023-12-29 | 2024-02-02 | 苏州映赛智能科技有限公司 | Road casting event identification method and device |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012081963A1 (en) * | 2010-12-15 | 2012-06-21 | Mimos Bhd. | System and method for traffic violation detection |
| JP2013168016A (en) * | 2012-02-15 | 2013-08-29 | Toyota Motor Corp | Travel lane recognition device |
| CN105528894A (en) * | 2015-11-28 | 2016-04-27 | 浙江宇视科技有限公司 | Operation detection method and device for front-end monitoring devices based on track information |
| CN105761483A (en) * | 2014-12-16 | 2016-07-13 | 高德软件有限公司 | Vehicle data processing method and equipment thereof |
| KR20160092959A (en) * | 2016-05-19 | 2016-08-05 | 윤종식 | Method of preventing traffic accidents in crossroad for signal violation and overspeed, and system of the same |
| CN205680290U (en) * | 2016-06-01 | 2016-11-09 | 纪峥嵘 | A kind of vehicle travels monitoring device and system |
| CN106327863A (en) * | 2015-07-06 | 2017-01-11 | 北京文安智能技术股份有限公司 | Method, device and system for detection and early warning of vehicle congestion |
| CN106485927A (en) * | 2016-10-28 | 2017-03-08 | 江苏信息职业技术学院 | A kind of intelligent transportation violation information harvester and acquisition method |
| CN107038870A (en) * | 2017-05-11 | 2017-08-11 | 南宁市正祥科技有限公司 | A kind of vehicle peccancy detection method |
| CN108074400A (en) * | 2016-11-14 | 2018-05-25 | 北京航天长峰科技工业集团有限公司 | A kind of emphasis vehicle analysis model based on mass data analysis |
| US20180357484A1 (en) * | 2016-02-02 | 2018-12-13 | Sony Corporation | Video processing device and video processing method |
| CN109087506A (en) * | 2018-07-26 | 2018-12-25 | 东软集团股份有限公司 | A kind of vehicle monitoring method and device |
| CN110599353A (en) * | 2018-06-13 | 2019-12-20 | 百度在线网络技术（北京）有限公司 | Vehicle insurance and claims rate prediction method, device, equipment and medium |
| CN110738857A (en) * | 2018-07-18 | 2020-01-31 | 杭州海康威视数字技术股份有限公司 | vehicle violation evidence obtaining method, device and equipment |
| CN111259767A (en) * | 2020-01-13 | 2020-06-09 | 厦门大学 | Traffic illegal behavior identification method and system based on traffic data and street view data |
| CN111627217A (en) * | 2020-05-29 | 2020-09-04 | 济南博观智能科技有限公司 | Method, device and equipment for judging vehicle illegal turning around and storage medium |
| CN111680633A (en) * | 2020-06-10 | 2020-09-18 | 上海眼控科技股份有限公司 | Vehicle violation identification method and device, computer equipment and storage medium |
| CN112185115A (en) * | 2020-09-29 | 2021-01-05 | 南通智大信息技术有限公司 | Vehicle speed control method and device based on cloud platform |
| US20210024082A1 (en) * | 2019-07-23 | 2021-01-28 | Toyota Jidosha Kabushiki Kaisha | Traffic signal interpretation system and vehicle control system |
-
2021
- 2021-04-25 CN CN202110450498.1A patent/CN113269042B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012081963A1 (en) * | 2010-12-15 | 2012-06-21 | Mimos Bhd. | System and method for traffic violation detection |
| JP2013168016A (en) * | 2012-02-15 | 2013-08-29 | Toyota Motor Corp | Travel lane recognition device |
| CN105761483A (en) * | 2014-12-16 | 2016-07-13 | 高德软件有限公司 | Vehicle data processing method and equipment thereof |
| CN106327863A (en) * | 2015-07-06 | 2017-01-11 | 北京文安智能技术股份有限公司 | Method, device and system for detection and early warning of vehicle congestion |
| CN105528894A (en) * | 2015-11-28 | 2016-04-27 | 浙江宇视科技有限公司 | Operation detection method and device for front-end monitoring devices based on track information |
| US20180357484A1 (en) * | 2016-02-02 | 2018-12-13 | Sony Corporation | Video processing device and video processing method |
| KR20160092959A (en) * | 2016-05-19 | 2016-08-05 | 윤종식 | Method of preventing traffic accidents in crossroad for signal violation and overspeed, and system of the same |
| CN205680290U (en) * | 2016-06-01 | 2016-11-09 | 纪峥嵘 | A kind of vehicle travels monitoring device and system |
| CN106485927A (en) * | 2016-10-28 | 2017-03-08 | 江苏信息职业技术学院 | A kind of intelligent transportation violation information harvester and acquisition method |
| CN108074400A (en) * | 2016-11-14 | 2018-05-25 | 北京航天长峰科技工业集团有限公司 | A kind of emphasis vehicle analysis model based on mass data analysis |
| CN107038870A (en) * | 2017-05-11 | 2017-08-11 | 南宁市正祥科技有限公司 | A kind of vehicle peccancy detection method |
| CN110599353A (en) * | 2018-06-13 | 2019-12-20 | 百度在线网络技术（北京）有限公司 | Vehicle insurance and claims rate prediction method, device, equipment and medium |
| CN110738857A (en) * | 2018-07-18 | 2020-01-31 | 杭州海康威视数字技术股份有限公司 | vehicle violation evidence obtaining method, device and equipment |
| CN109087506A (en) * | 2018-07-26 | 2018-12-25 | 东软集团股份有限公司 | A kind of vehicle monitoring method and device |
| US20210024082A1 (en) * | 2019-07-23 | 2021-01-28 | Toyota Jidosha Kabushiki Kaisha | Traffic signal interpretation system and vehicle control system |
| CN111259767A (en) * | 2020-01-13 | 2020-06-09 | 厦门大学 | Traffic illegal behavior identification method and system based on traffic data and street view data |
| CN111627217A (en) * | 2020-05-29 | 2020-09-04 | 济南博观智能科技有限公司 | Method, device and equipment for judging vehicle illegal turning around and storage medium |
| CN111680633A (en) * | 2020-06-10 | 2020-09-18 | 上海眼控科技股份有限公司 | Vehicle violation identification method and device, computer equipment and storage medium |
| CN112185115A (en) * | 2020-09-29 | 2021-01-05 | 南通智大信息技术有限公司 | Vehicle speed control method and device based on cloud platform |
Non-Patent Citations (2)
| Title |
|---|
| ZHIHAN JIANG ET AL.: "iTV: Inferring Traffic Violation-Prone Locations With Vehicle Trajectories and Road Environment Data", 《ARXIV.ORG》, pages 1 - 12 * |
| 张宁等: "基于OGS-DTW算法的交通事件自动检测方法", 《公路交通科技》, vol. 27, no. 8, pages 103 - 108 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114255588A (en) * | 2021-11-29 | 2022-03-29 | 安徽百诚慧通科技有限公司 | Regional motor vehicle illegal parking prediction method, storage medium and device |
| CN115114312A (en) * | 2022-07-15 | 2022-09-27 | 北京百度网讯科技有限公司 | Map data updating method and device and electronic equipment |
| CN117494029A (en) * | 2023-12-29 | 2024-02-02 | 苏州映赛智能科技有限公司 | Road casting event identification method and device |
| CN117494029B (en) * | 2023-12-29 | 2024-04-19 | 苏州映赛智能科技有限公司 | Road casting event identification method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113269042B (en) | 2024-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113269042B (en) | Intelligent traffic management method and system based on driving vehicle violation identification | |
| US9365217B2 (en) | Mobile pothole detection system and method | |
| US10163038B2 (en) | Error detection in recognition data | |
| CN111832492B (en) | Static traffic abnormality judging method and device, computer equipment and storage medium | |
| CN112541372B (en) | Difficult sample screening method and device | |
| EP4020428A1 (en) | Method and apparatus for recognizing lane, and computing device | |
| CN112529109A (en) | Unsupervised multi-model-based anomaly detection method and system | |
| CN110443319B (en) | Track duplicate removal method and device and storage medium | |
| CN112447060A (en) | Method and device for recognizing lane and computing equipment | |
| CN111967451B (en) | Road congestion detection method and device | |
| CN115841765B (en) | Vehicle position blind area monitoring method and device, electronic equipment and readable storage medium | |
| CN116563809A (en) | Road disease identification method and device, electronic equipment and storage medium | |
| CN116432095A (en) | Road disease prediction method, device, electronic equipment and storage medium | |
| CN114066288B (en) | Intelligent data center-based emergency detection method and system for operation road | |
| CN112927498B (en) | Data analysis method and device based on intelligent traffic monitoring | |
| CN113051307A (en) | Alarm signal analysis method, equipment, storage medium and device | |
| CN114407918B (en) | Takeover scene analysis method, takeover scene analysis device, takeover scene analysis equipment and storage medium | |
| CN112926823B (en) | Intelligent traffic service data detection method and device and electronic equipment | |
| CN116503695B (en) | Training method of target detection model, target detection method and device | |
| CN116071542A (en) | Highway casting object detection method and device based on vision and storage medium | |
| WO2024139287A1 (en) | Road disease prediction method and apparatus, electronic device and storage medium | |
| CN115908359A (en) | Method, device, equipment and medium for detecting water spraying state of vehicle windscreen wiper | |
| CN117671308A (en) | Automatic driving test scene construction method and device, electronic equipment and medium | |
| CN116228810A (en) | Target tracking method, device, equipment and medium for single-row area | |
| CN116824847A (en) | Method and device for processing traffic flow parameters of expressway |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |