CN105632175A - Vehicle behavior analysis method and system - Google Patents
Vehicle behavior analysis method and system Download PDFInfo
- Publication number
- CN105632175A CN105632175A CN201610011764.XA CN201610011764A CN105632175A CN 105632175 A CN105632175 A CN 105632175A CN 201610011764 A CN201610011764 A CN 201610011764A CN 105632175 A CN105632175 A CN 105632175A
- Authority
- CN
- China
- Prior art keywords
- car plate
- vehicle
- car
- positional information
- image
- 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
Classifications
-
- 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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
-
- 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
-
- 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
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a vehicle behavior analysis method, comprising steps of obtaining a vehicle image in real time, obtaining license plate position information in the image, performing comparison between the license plate position information and history license plate position information in a tracking list, adding the license plate position information into a corresponding matched license plate if a matched license plate exists, adding a new license plate item in the tracking list if the matched license plate does not exist, identifying the license plate number in the image according to the newest license plate position, extracting the license plate characteristic points of each license plate position information in order to construct a license plate motion track, and determining the vehicle behavior of the license plate according to the license plate motion track and the preset behavior. The invention can perform analysis based on the result of the license plate detection, and is high in accuracy and low in costs of system and maintenance.
Description
Technical field
The present invention relates to the technical field of intelligent transportation, particularly to be vehicle behavior analysis method and system.
Background technology
Intellectual technology reaches its maturity in the development of field of traffic, is mainly used in highway bayonet socket or gateway, parking lot. Current main flow intelligent transportation settling mode is, ground induction coil perceive vehicle through information after, send signal and pass to wagon detector, it is that a car passes through that wagon detector is analyzed to identify, then send to touch and signal to video camera, video camera receives signal, controls light compensating lamp and sends light, while pressing shutter shooting image and identifying car plate. Such as the method, vehicle is taken through out-of-date and is captured to a pictures, and the differentiation kind of vehicle behavior is very limited, and an equipment can only realize one or two single intelligent video function; Construction trouble, camera, vertical rod, lamp, ground induction coil etc. are required for accurate placement, and the software of camera is also required to a lot of setting; System cost is high, video camera, lamp, cross bar, radar etc., and every the same price is all high; Maintenance cost is very high, and for intelligent transportation system company, maintenance cost account for more than the 50% of total expenditure; Poor stability, because system is too complicated, as long as there being an equipment out of joint, or certain two equipment coordinates out of joint, and whole system cannot normal operation.
Additionally also by the driving trace of the mode registration of vehicle of polyphaser linkage, application number as disclosed in Patent Office of the People's Republic of China is traffic video behavior analysis and the Alarm Server of CN201110026713, but still existing defects: the installation of hardware and maintenance cost are high, it is necessary to multiple cameras; Poor stability, has a camera to go wrong, and whole system cannot normal operation; And software realizes complexity.
Summary of the invention
The technical problem to be solved is to provide a kind of vehicle behavior analysis method, it is possible to vehicle behavior is analyzed by the result based on car plate detection, and accuracy rate is high, and system and maintenance cost are relatively low.
For solving the problems referred to above, the present invention proposes a kind of vehicle behavior analysis method, comprises the following steps:
S1: obtaining vehicle image in real time, detection obtains the car plate positional information in image;
S2: new car board positional information is compared with the history car plate positional information followed the tracks of in list, if there is coupling car plate Xiang Ze new car board positional information is inserted the corresponding coupling car plate item following the tracks of in list, if without coupling car plate, adding new car plate item in following the tracks of list;
S3: according to the license plate number in up-to-date car plate location recognition image;
S4: extract the vehicle license plate characteristic point of each car plate positional information to build car plate movement locus;
S5: according to car plate movement locus and default behavior decision rule, it is determined that the behavior of the vehicle of this license plate number.
According to one embodiment of present invention, also including step S6: the image obtaining unlawful practice vehicle is compressed, the image/video obtaining monitoring scene is compressed, and forms, with violation information, the behavior analysis object information that text describes according to information of vehicles.
According to one embodiment of present invention, between step S2 and S3, also include step S21: carry out car plate position correction according to new car board positional information and the history car plate positional information followed the tracks of in list, to obtain the up-to-date car plate position after correcting.
According to one embodiment of present invention, described car plate positional information includes the coordinate at least one summit on car plate, and car plate width and car plate height.
According to one embodiment of present invention, in described step S2, new car board positional information is compared according to car plate method for estimating and feature matching method with the history car plate positional information followed the tracks of in list, if new car board positional information has motion continuity with the history car plate positional information followed the tracks of in list and vehicle license plate characteristic mates concordance, then this car plate item is judged as coupling car plate item.
According to one embodiment of present invention, described step S5 includes:
The judgement of vehicle heading, to shoot the camera of image as object of reference, camera erection towards as reference orientation, according to car plate moving direction in the picture and described reference orientation, it is determined that vehicle heading; And/or,
The judgement of vehicle lane change, using the lane line in image as line of reference, when the continuous multiple points on car plate movement locus are in the side of lane line, then, the continuous multiple points on the movement locus of car plate at lane line at opposite side, it is determined that for vehicle lane change; And/or,
The judgement that vehicle drives in the wrong direction, according to car plate running orbit, it is judged that the traffic direction of vehicle, when car plate running orbit line takes the shape of the letter U, it is determined that drive in the wrong direction for vehicle; And/or,
The judgement of overspeed of vehicle, carry out the image space conversion to real physical space, need to calculate the conversion parameter matrix in two spaces, four summits according to known length and the lane line of width calculate speed, car plate movement locus according to image and conversion parameter matrix, be converted to the image pixel difference of each adjacent two frames the difference of real physical space, calculate speed according to frame per second, when speed is more than predetermined threshold value, it is determined that for overspeed of vehicle.
According to one embodiment of present invention, also including: vehicle flowrate after described step S5, according to license plate number recognition result, the license plate number of process in monitoring scene is added up, the corresponding unique vehicle of each license plate number, thus realizing vehicle flowrate.
The present invention also provides for a kind of vehicle behavior analysis system, including:
Car plate detection module, obtains vehicle image in real time, and detection obtains the car plate positional information in image;
Car plate tracking module, receive the car plate positional information of described car plate detection module, new car board positional information is compared with the history car plate positional information followed the tracks of in list, if there is coupling car plate Xiang Ze new car board positional information is inserted the corresponding coupling car plate item following the tracks of in list, if without coupling car plate, adding new car plate item in following the tracks of list;
Car license recognition module, receives the car plate positional information of described car plate tracking module, according to the license plate number in up-to-date car plate location recognition image;
Trajectory extraction module, receives the car plate positional information of described car plate tracking module, extracts the vehicle license plate characteristic point of each car plate positional information to build car plate movement locus;
Behavior analysis module, receives the car plate movement locus of described trajectory extraction module, according to car plate movement locus and default behavior decision rule, it is determined that the behavior of the vehicle of this license plate number.
According to one embodiment of present invention, also include information describing module, the image obtaining unlawful practice vehicle is compressed, and forms, with the violation information of the behavior analysis module of reception, the behavior analysis object information that text describes according to the information of vehicles of the Car license recognition module received.
According to one embodiment of present invention, also include result output module, obtain the compression video of monitoring scene, receive compression image and the behavior analysis object information of described information describing module, received and display monitoring scene video by network, and epideictic behaviour analyzes the image of object information and correspondence thereof.
According to one embodiment of present invention, also including vehicle flowrate module, according to license plate number recognition result, the license plate number of process in monitoring scene is added up, the corresponding unique vehicle of each license plate number, thus realizing vehicle flowrate.
According to one embodiment of present invention, also include position correction module, new car board positional information according to the car plate tracking module received and the history car plate positional information followed the tracks of in list, the new car board positional information of the car plate detection module received is carried out car plate position correction, to obtain the up-to-date car plate position after correcting; Described Car license recognition module is according to the license plate number in the up-to-date car plate location recognition image of the position correction module received.
After adopting technique scheme, the present invention has the advantages that the vehicle image that can obtain high definition from monitoring scene in real time compared to existing technology, the every two field picture obtained detects the car plate of vehicle, car plate is detected by original high-resolution image, high-definition picture ensure that the integrity of license plate area information, car plate is tracked record, according to up-to-date car plate location recognition license plate number, build the car plate movement locus of this license plate number, such that it is able to judge the vehicle behavior of this license plate number, vehicle behavior is analyzed by the result based on car plate detection, such as lane change, drive in the wrong direction, hypervelocity, vehicle flowrate, can detection license board information while analysis monitoring vehicle behavior, thus easily and timely obtaining rule-breaking vehicle information, behavior analysis accuracy rate is high, and the cost of the equipment of installation and maintenance is all relatively low.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the vehicle behavior analysis method of one embodiment of the invention;
Fig. 2 is the structured flowchart of the vehicle behavior analysis system of one embodiment of the invention;
Fig. 3 is the structured flowchart of the vehicle behavior analysis system of another embodiment of the present invention.
Detailed description of the invention
Understandable for enabling the above-mentioned purpose of the present invention, feature and advantage to become apparent from, below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail.
Elaborate a lot of detail in the following description so that fully understanding the present invention. But the present invention can implement being much different from alternate manner described here, and those skilled in the art can do similar popularization when without prejudice to intension of the present invention, therefore the present invention is by the following public restriction being embodied as.
Referring to Fig. 1, the vehicle behavior analysis method of the present embodiment, comprise the following steps:
S1: obtaining vehicle image in real time, detection obtains the car plate positional information in image;
S2: new car board positional information is compared with the history car plate positional information followed the tracks of in list, if there is coupling car plate Xiang Ze new car board positional information is inserted the corresponding coupling car plate item following the tracks of in list, if without coupling car plate, adding new car plate item in following the tracks of list;
S3: according to the license plate number in up-to-date car plate location recognition image;
S4: extract the vehicle license plate characteristic point of each car plate positional information to build car plate movement locus;
S5: according to car plate movement locus and default behavior decision rule, it is determined that the behavior of the vehicle of this license plate number.
Vehicle image can obtain from the monitoring scene video of high-definition camera captured in real-time, the image in video is obtained according to certain frame per second, image is high-definition picture, and detection and subsequent treatment all carry out on high-definition picture, and car plate detection is image detection frame by frame in real time.
In step sl, car plate is detected by the vehicle image of the original high resolution obtained in real time, high-definition picture ensure that the integrity of license plate area information, the car plate positional information detected for follow-up tracking, car plate positional information can be such as the left summit of car plate x coordinate in the picture and y-coordinate (can certainly be other summits, or the coordinate on multiple summits), car plate width, car plate height. Can the position of the whole framework of positioning licence plate in the picture according to car plate positional information.
In step s 2, pre-build tracking list, if what in image, the car plate of process was new then sets up a car plate item, car plate item records the car plate positional information that each two field picture obtains, after obtaining new car board positional information, compare with the history license board information put into before in the car plate item following the tracks of list, there is coupling car plate item, namely match same car plate, new license board information is put into corresponding coupling car plate item in tracking list, new car plate regarded as by the car plate not matched, i.e. vehicle in newly entering video image, a new car plate item is added following the tracks of list, realize the tracking to car plate.
Wherein, new car board positional information can be compared according to car plate method for estimating and feature matching method with the history car plate positional information followed the tracks of in list, if new car board positional information has motion continuity with the history car plate positional information followed the tracks of in list and vehicle license plate characteristic mates concordance, then this car plate item is judged as coupling car plate item.
In step s3, new car board positional information according to obtaining in step S2 identifies license plate number in the picture, owing to having continuously performed multiframe tracking, it is identified obtaining license plate number to each character of car plate, in this course, can be undertaken multiframe recognition result comprehensively, selecting the most believable result, thus ensureing the lifting of the recognition accuracy to each character of car plate.
Before step S3, car plate position can be corrected, according to the history car plate positional information in new car board positional information and tracking list, car plate in image is carried out car plate position correction, to obtain the up-to-date car plate position after correcting, the car plate position after this correction is identified the car plate position of license plate number in image in step S3, car plate position has been corrected, car plate position is more accurate, is further ensured that the accuracy rate of Car license recognition.
In step s 4, the car plate positional information after VLP correction can be obtained, or can also be not calibrated car plate positional information, extract the vehicle license plate characteristic point of each car plate positional information, the motion of the characteristic point of each two field picture is configured to car plate movement locus. Vehicle license plate characteristic point is such as the left apex coordinate of car plate, certainly can also is that other location points on car plate or region.
In step s 5, according to the car plate movement locus obtained in step S4 and default behavior decision rule, it is determined that the behavior of the vehicle of this license plate number, preset behavior decision rule and can set that object of reference, the conversion of image and real scene, and relevant parameter etc.
Concrete, the judgement content of vehicle behavior and decision procedure are such as included the judgement of following content:
The judgement of vehicle heading, to shoot the camera of image as object of reference, camera erection towards as reference orientation, such as it is divided into: to the south, northwards, eastwards, westwards, according to car plate moving direction in the picture and described reference orientation, judge vehicle heading, vehicle travel direction on two-way lane or unidirectional track can be judged, as set camera as time to the south, then when vehicle is by when on image, directional image moves below, namely speed is forward, vehicle heading can be labeled as by south to north, otherwise it is by north to south, other directions are similar, and/or,
The judgement of vehicle lane change, using the lane line in image as line of reference, pass through software interface, on input lane line, any two points realizes the demarcation of lane line, when the continuous multiple points on car plate movement locus are in the side of lane line, then, the continuous multiple points on the movement locus of car plate at lane line at opposite side, it is determined that for vehicle lane change; And/or,
The judgement that vehicle drives in the wrong direction, according to car plate running orbit, it is judged that the traffic direction of vehicle, when car plate running orbit line takes the shape of the letter U, it is determined that drive in the wrong direction for vehicle; And/or,
The judgement of overspeed of vehicle, carry out the image space conversion to real physical space, need to calculate the conversion parameter matrix in two spaces, four summits according to known length and the lane line of width calculate speed, car plate movement locus according to image and conversion parameter matrix, be converted to the image pixel difference of each adjacent two frames the difference of real physical space, calculate speed according to frame per second, when speed is more than predetermined threshold value, it is determined that for overspeed of vehicle.
In a further embodiment, also include step S6, the process to behavior analysis result, can be used for showing output etc. According to behavior analysis result, obtain the image of unlawful practice vehicle, and this image is compressed, monitoring scene video can be obtained simultaneously, equally video being compressed, compression, to alleviate transmitted data amount burden, forms, with violation information, the behavior analysis object information that text describes according to information of vehicles, compressed picture, compression video and behavior analysis object information text can be transmitted by network, it is provided that display to display platform.
Optionally, also include after step s 5: vehicle flowrate, according to license plate number recognition result, the license plate number of process in monitoring scene is added up, the corresponding unique vehicle of each license plate number, thus realizing vehicle flowrate, and then a certain road degree of crowding specifying the time period can be judged.
Referring to Fig. 2, the vehicle behavior analysis system of the present embodiment, including car plate detection module 1, car plate tracking module 2, Car license recognition module 4, trajectory extraction module 3 and behavior analysis module 5.
Car plate detection module 1 obtains vehicle image in real time, and detection obtains the car plate positional information in image. Vehicle image is equally possible is the high-resolution image obtained frame by frame from the HD video of high-definition camera 100 shooting.
Car plate tracking module 2 receives the car plate positional information of car plate detection module 1, car plate tracking module 2 has pre-build tracking list, and the car plate item followed the tracks of in list along with the conversion of vehicle and updating in image, new car board positional information is compared with the history car plate positional information followed the tracks of in list, if there is coupling car plate Xiang Ze new car board positional information is inserted the corresponding coupling car plate item following the tracks of in list, if without coupling car plate, adding new car plate item in following the tracks of list, one car plate item can be a data queue, it is used for storing the car plate positional information of the current of a car plate and history.
Car license recognition module 4 receives the car plate positional information of car plate tracking module 2, and according to the license plate number in up-to-date car plate location recognition image, this license plate number can be used for associating with subsequent vehicle behavior analysis result. Trajectory extraction module 3 receives the car plate positional information of car plate tracking module 2, extracts the vehicle license plate characteristic point of car plate positional information in each two field picture, builds car plate movement locus with the change of vehicle license plate characteristic point. The car plate movement locus of behavior analysis module 5 receiving locus extraction module 3, according to car plate movement locus and default behavior decision rule, it is determined that the behavior of the vehicle of this license plate number.
In one embodiment, referring to Fig. 2, vehicle behavior analysis system also includes information describing module 6, the image obtaining unlawful practice vehicle is compressed forming compressed picture, and the violation information according to the information of vehicles (license plate number) of the Car license recognition module 4 received, the behavior analysis module 5 of reception, form the behavior analysis object information that textual form describes.
Referring to Fig. 3, vehicle behavior analysis system also includes can result output module 8, obtain the compression video of monitoring scene, the compression image of reception information describing module 6 and behavior analysis object information, received and display monitoring scene video by network, and epideictic behaviour analyzes the image of object information and correspondence thereof. The behavior analysis object information of compression image and textual form can be transferred to result output module 8 by network by information describing module 6, and result output module 8 can be the computer platform with display screen. Preferably, video compressing module 9 it is additionally provided with between high-definition camera 100 and result output module 8, video compressing module 9 is sent to result output module 8 after being compressed by the HD video that high-definition camera 100 shoots, again may be by network or other forms send, while detection car plate, the video gathered is compressed, is sent to result output module 8.
Vehicle behavior analysis system can also include vehicle flowrate module (not shown), according to license plate number recognition result, the license plate number of process in monitoring scene is added up, and the corresponding unique vehicle of each license plate number, thus realizing vehicle flowrate.
Further, with continued reference to Fig. 3, vehicle behavior analysis system can also include position correction module 7, new car board positional information according to the car plate tracking module 2 received and the history car plate positional information followed the tracks of in list, in conjunction with current information and historical information correction error, the new car board positional information of the car plate detection module 1 received is carried out car plate position correction, to obtain the up-to-date car plate position after correcting; Car license recognition module 4 receives the car plate positional information after position correction module 7 correction, according to the license plate number in up-to-date car plate location recognition image; Trajectory extraction module 3 receives the car plate positional information after position correction module 7 correction, builds car plate movement locus according to up-to-date car plate positional information.
What deserves to be explained is, on describing, the System and method for content repeating part of the present invention has been carried out brief description, and system particular content is referred to the content of method part. The modes such as the image recognition in conventional images treatment technology, feature extraction, estimation can be adopted to realize the Car license recognition of the present invention, and positional information extracts, matching ratio equity. The present invention can the behavior of analysis monitoring vehicle while detection car plate.
Although the present invention is with preferred embodiment openly as above; but it is not for limiting claim; any those skilled in the art are without departing from the spirit and scope of the present invention; can making possible variation and amendment, therefore protection scope of the present invention should be as the criterion with the scope that the claims in the present invention define.
Claims (12)
1. a vehicle behavior analysis method, it is characterised in that comprise the following steps:
S1: obtaining vehicle image in real time, detection obtains the car plate positional information in image;
S2: new car board positional information is compared with the history car plate positional information followed the tracks of in list, if there is coupling car plate Xiang Ze new car board positional information is inserted the corresponding coupling car plate item following the tracks of in list, if without coupling car plate, adding new car plate item in following the tracks of list;
S3: according to the license plate number in up-to-date car plate location recognition image;
S4: extract the vehicle license plate characteristic point of each car plate positional information to build car plate movement locus;
S5: according to car plate movement locus and default behavior decision rule, it is determined that the behavior of the vehicle of this license plate number.
2. vehicle behavior analysis method as claimed in claim 1, it is characterized in that, also including step S6: the image obtaining unlawful practice vehicle is compressed, the image/video obtaining monitoring scene is compressed, and forms, with violation information, the behavior analysis object information that text describes according to information of vehicles.
3. vehicle behavior analysis method as claimed in claim 1, it is characterized in that, step S21 is also included: carry out car plate position correction according to new car board positional information and the history car plate positional information followed the tracks of in list, to obtain the up-to-date car plate position after correcting between step S2 and S3.
4. vehicle behavior analysis method as claimed in claim 1, it is characterised in that described car plate positional information includes the coordinate at least one summit on car plate, and car plate width and car plate height.
5. vehicle behavior analysis method as claimed in claim 1, it is characterized in that, in described step S2, new car board positional information is compared according to car plate method for estimating and feature matching method with the history car plate positional information followed the tracks of in list, if new car board positional information has motion continuity with the history car plate positional information followed the tracks of in list and vehicle license plate characteristic mates concordance, then this car plate item is judged as coupling car plate item.
6. vehicle behavior analysis method as claimed in claim 1, it is characterised in that described step S5 includes:
The judgement of vehicle heading, to shoot the camera of image as object of reference, camera erection towards as reference orientation, according to car plate moving direction in the picture and described reference orientation, it is determined that vehicle heading; And/or,
The judgement of vehicle lane change, using the lane line in image as line of reference, when the continuous multiple points on car plate movement locus are in the side of lane line, then, the continuous multiple points on the movement locus of car plate at lane line at opposite side, it is determined that for vehicle lane change; And/or,
The judgement that vehicle drives in the wrong direction, according to car plate running orbit, it is judged that the traffic direction of vehicle, when car plate running orbit line takes the shape of the letter U, it is determined that drive in the wrong direction for vehicle; And/or,
The judgement of overspeed of vehicle, carry out the image space conversion to real physical space, need to calculate the conversion parameter matrix in two spaces, four summits according to known length and the lane line of width calculate speed, car plate movement locus according to image and conversion parameter matrix, be converted to the image pixel difference of each adjacent two frames the difference of real physical space, calculate speed according to frame per second, when speed is more than predetermined threshold value, it is determined that for overspeed of vehicle.
7. the vehicle behavior analysis method as described in claim 1 or 6, it is characterized in that, also include after described step S5: vehicle flowrate, according to license plate number recognition result, the license plate number of process in monitoring scene is added up, the corresponding unique vehicle of each license plate number, thus realizing vehicle flowrate.
8. a vehicle behavior analysis system, it is characterised in that including:
Car plate detection module, obtains vehicle image in real time, and detection obtains the car plate positional information in image;
Car plate tracking module, receive the car plate positional information of described car plate detection module, new car board positional information is compared with the history car plate positional information followed the tracks of in list, if there is coupling car plate Xiang Ze new car board positional information is inserted the corresponding coupling car plate item following the tracks of in list, if without coupling car plate, adding new car plate item in following the tracks of list;
Car license recognition module, receives the car plate positional information of described car plate tracking module, according to the license plate number in up-to-date car plate location recognition image;
Trajectory extraction module, receives the car plate positional information of described car plate tracking module, extracts the vehicle license plate characteristic point of each car plate positional information to build car plate movement locus;
Behavior analysis module, receives the car plate movement locus of described trajectory extraction module, according to car plate movement locus and default behavior decision rule, it is determined that the behavior of the vehicle of this license plate number.
9. vehicle behavior analysis system as claimed in claim 8, it is characterized in that, also include information describing module, the image obtaining unlawful practice vehicle is compressed, and forms, with the violation information of the behavior analysis module of reception, the behavior analysis object information that text describes according to the information of vehicles of the Car license recognition module received.
10. vehicle behavior analysis system as claimed in claim 9, it is characterized in that, also include result output module, obtain the compression video of monitoring scene, receive compression image and the behavior analysis object information of described information describing module, received and display monitoring scene video by network, and epideictic behaviour analyzes the image of object information and correspondence thereof.
11. vehicle behavior analysis system as claimed in claim 10, it is characterised in that also include vehicle flowrate module, according to license plate number recognition result, the license plate number of process in monitoring scene is added up, and the corresponding unique vehicle of each license plate number, thus realizing vehicle flowrate.
12. vehicle behavior analysis system as claimed in claim 8, it is characterized in that, also include position correction module, new car board positional information according to the car plate tracking module received and the history car plate positional information followed the tracks of in list, the new car board positional information of the car plate detection module received is carried out car plate position correction, to obtain the up-to-date car plate position after correcting; Described Car license recognition module is according to the license plate number in the up-to-date car plate location recognition image of the position correction module received.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610011764.XA CN105632175B (en) | 2016-01-08 | 2016-01-08 | Vehicle behavior analysis method and system |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610011764.XA CN105632175B (en) | 2016-01-08 | 2016-01-08 | Vehicle behavior analysis method and system |
Publications (2)
Publication Number | Publication Date |
---|---|
CN105632175A true CN105632175A (en) | 2016-06-01 |
CN105632175B CN105632175B (en) | 2019-03-29 |
Family
ID=56047048
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610011764.XA Active CN105632175B (en) | 2016-01-08 | 2016-01-08 | Vehicle behavior analysis method and system |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105632175B (en) |
Cited By (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106251633A (en) * | 2016-08-09 | 2016-12-21 | 成都联众智科技有限公司 | License auto-recognition system and the system of tracking |
CN106295810A (en) * | 2016-07-25 | 2017-01-04 | 国网山东省电力公司青岛供电公司 | A kind of troublshooting work order distributing method and device |
CN106529401A (en) * | 2016-09-26 | 2017-03-22 | 北京格灵深瞳信息技术有限公司 | Vehicle anti-tracking method, vehicle anti-tracking device and vehicle anti-tracking system |
CN106781513A (en) * | 2016-11-28 | 2017-05-31 | 东南大学 | The recognition methods of vehicle behavior in a kind of urban transportation scene of feature based fusion |
CN107424412A (en) * | 2017-09-21 | 2017-12-01 | 程丹秋 | A kind of traffic behavior analysis system |
CN107635188A (en) * | 2017-09-08 | 2018-01-26 | 安徽四创电子股份有限公司 | A kind of video frequency vehicle trace analysis method based on Docker platforms |
CN107862862A (en) * | 2016-09-22 | 2018-03-30 | 杭州海康威视数字技术股份有限公司 | A kind of vehicle behavior analysis method and device |
CN107886055A (en) * | 2017-10-27 | 2018-04-06 | 中国科学院声学研究所 | A kind of retrograde detection method judged for direction of vehicle movement |
CN108548543A (en) * | 2018-03-27 | 2018-09-18 | 上海识加电子科技有限公司 | vehicle route management method and device |
CN108846906A (en) * | 2018-06-04 | 2018-11-20 | 西安艾润物联网技术服务有限责任公司 | Parking management method, system and computer readable storage medium |
CN109747642A (en) * | 2019-01-04 | 2019-05-14 | 北京博宇通达科技有限公司 | Vehicle travel control method and equipment |
CN109887303A (en) * | 2019-04-18 | 2019-06-14 | 齐鲁工业大学 | Random change lane behavior early warning system and method |
CN110728843A (en) * | 2019-09-10 | 2020-01-24 | 浙江大华技术股份有限公司 | Vehicle snapshot method, vehicle snapshot device, and storage medium |
CN110807934A (en) * | 2019-11-13 | 2020-02-18 | 赵洪涛 | Intelligent scheduling platform for monitoring highway operation |
CN111127949A (en) * | 2019-12-18 | 2020-05-08 | 北京中交兴路车联网科技有限公司 | Vehicle high-risk road section early warning method and device and storage medium |
CN111145555A (en) * | 2019-12-09 | 2020-05-12 | 浙江大华技术股份有限公司 | Method and device for detecting vehicle violation |
CN111539337A (en) * | 2020-04-26 | 2020-08-14 | 上海眼控科技股份有限公司 | Vehicle posture correction method, device and equipment |
CN111640300A (en) * | 2020-04-28 | 2020-09-08 | 武汉万集信息技术有限公司 | Vehicle detection processing method and device |
CN111815960A (en) * | 2020-06-28 | 2020-10-23 | 贵州数据宝网络科技有限公司 | Vehicle information display method based on big data |
CN113366548A (en) * | 2018-12-28 | 2021-09-07 | 北京航迹科技有限公司 | System and method for vehicle identification |
CN113591820A (en) * | 2021-09-30 | 2021-11-02 | 深圳市鑫道为科技有限公司 | Driving data storage method capable of extracting hit-and-run license plate information |
CN114170810A (en) * | 2021-12-28 | 2022-03-11 | 深圳市捷顺科技实业股份有限公司 | Vehicle traveling direction identification method, system and device |
CN115762143A (en) * | 2022-11-02 | 2023-03-07 | 宁波云弧科技有限公司 | Vehicle track processing method |
CN116071688A (en) * | 2023-03-06 | 2023-05-05 | 台州天视智能科技有限公司 | Behavior analysis method and device for vehicle, electronic equipment and storage medium |
US11709828B2 (en) | 2019-10-31 | 2023-07-25 | Genetec Inc | Method and system for associating a license plate number with a user |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060278705A1 (en) * | 2003-02-21 | 2006-12-14 | Accenture Global Services Gmbh | Electronic Toll Management and Vehicle Identification |
CN101877174A (en) * | 2009-09-29 | 2010-11-03 | 杭州海康威视软件有限公司 | Vehicle speed measurement method, supervisory computer and vehicle speed measurement system |
CN102509457A (en) * | 2011-10-09 | 2012-06-20 | 青岛海信网络科技股份有限公司 | Vehicle tracking method and device |
CN102521979A (en) * | 2011-12-06 | 2012-06-27 | 北京万集科技股份有限公司 | High-definition camera-based method and system for pavement event detection |
CN203606951U (en) * | 2013-11-06 | 2014-05-21 | 杭州海康威视数字技术股份有限公司 | Image acquisition device based on license plate identification and system |
-
2016
- 2016-01-08 CN CN201610011764.XA patent/CN105632175B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060278705A1 (en) * | 2003-02-21 | 2006-12-14 | Accenture Global Services Gmbh | Electronic Toll Management and Vehicle Identification |
CN101877174A (en) * | 2009-09-29 | 2010-11-03 | 杭州海康威视软件有限公司 | Vehicle speed measurement method, supervisory computer and vehicle speed measurement system |
CN102509457A (en) * | 2011-10-09 | 2012-06-20 | 青岛海信网络科技股份有限公司 | Vehicle tracking method and device |
CN102521979A (en) * | 2011-12-06 | 2012-06-27 | 北京万集科技股份有限公司 | High-definition camera-based method and system for pavement event detection |
CN203606951U (en) * | 2013-11-06 | 2014-05-21 | 杭州海康威视数字技术股份有限公司 | Image acquisition device based on license plate identification and system |
Cited By (31)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106295810A (en) * | 2016-07-25 | 2017-01-04 | 国网山东省电力公司青岛供电公司 | A kind of troublshooting work order distributing method and device |
CN106295810B (en) * | 2016-07-25 | 2020-09-08 | 国网山东省电力公司青岛供电公司 | Fault repair work order distribution method and device |
CN106251633A (en) * | 2016-08-09 | 2016-12-21 | 成都联众智科技有限公司 | License auto-recognition system and the system of tracking |
CN107862862A (en) * | 2016-09-22 | 2018-03-30 | 杭州海康威视数字技术股份有限公司 | A kind of vehicle behavior analysis method and device |
CN107862862B (en) * | 2016-09-22 | 2020-11-20 | 杭州海康威视数字技术股份有限公司 | Vehicle behavior analysis method and device |
CN106529401A (en) * | 2016-09-26 | 2017-03-22 | 北京格灵深瞳信息技术有限公司 | Vehicle anti-tracking method, vehicle anti-tracking device and vehicle anti-tracking system |
CN106781513A (en) * | 2016-11-28 | 2017-05-31 | 东南大学 | The recognition methods of vehicle behavior in a kind of urban transportation scene of feature based fusion |
CN107635188A (en) * | 2017-09-08 | 2018-01-26 | 安徽四创电子股份有限公司 | A kind of video frequency vehicle trace analysis method based on Docker platforms |
CN107424412A (en) * | 2017-09-21 | 2017-12-01 | 程丹秋 | A kind of traffic behavior analysis system |
CN107886055A (en) * | 2017-10-27 | 2018-04-06 | 中国科学院声学研究所 | A kind of retrograde detection method judged for direction of vehicle movement |
CN108548543A (en) * | 2018-03-27 | 2018-09-18 | 上海识加电子科技有限公司 | vehicle route management method and device |
CN108846906A (en) * | 2018-06-04 | 2018-11-20 | 西安艾润物联网技术服务有限责任公司 | Parking management method, system and computer readable storage medium |
CN108846906B (en) * | 2018-06-04 | 2021-07-30 | 西安艾润物联网技术服务有限责任公司 | Parking management method, system and computer readable storage medium |
CN113366548A (en) * | 2018-12-28 | 2021-09-07 | 北京航迹科技有限公司 | System and method for vehicle identification |
CN109747642A (en) * | 2019-01-04 | 2019-05-14 | 北京博宇通达科技有限公司 | Vehicle travel control method and equipment |
CN109887303A (en) * | 2019-04-18 | 2019-06-14 | 齐鲁工业大学 | Random change lane behavior early warning system and method |
CN110728843B (en) * | 2019-09-10 | 2021-08-31 | 浙江大华技术股份有限公司 | Vehicle snapshot method, vehicle snapshot device, and storage medium |
CN110728843A (en) * | 2019-09-10 | 2020-01-24 | 浙江大华技术股份有限公司 | Vehicle snapshot method, vehicle snapshot device, and storage medium |
US11709828B2 (en) | 2019-10-31 | 2023-07-25 | Genetec Inc | Method and system for associating a license plate number with a user |
CN110807934A (en) * | 2019-11-13 | 2020-02-18 | 赵洪涛 | Intelligent scheduling platform for monitoring highway operation |
CN111145555B (en) * | 2019-12-09 | 2021-02-26 | 浙江大华技术股份有限公司 | Method and device for detecting vehicle violation |
CN111145555A (en) * | 2019-12-09 | 2020-05-12 | 浙江大华技术股份有限公司 | Method and device for detecting vehicle violation |
CN111127949A (en) * | 2019-12-18 | 2020-05-08 | 北京中交兴路车联网科技有限公司 | Vehicle high-risk road section early warning method and device and storage medium |
CN111539337A (en) * | 2020-04-26 | 2020-08-14 | 上海眼控科技股份有限公司 | Vehicle posture correction method, device and equipment |
CN111640300A (en) * | 2020-04-28 | 2020-09-08 | 武汉万集信息技术有限公司 | Vehicle detection processing method and device |
CN111815960A (en) * | 2020-06-28 | 2020-10-23 | 贵州数据宝网络科技有限公司 | Vehicle information display method based on big data |
CN113591820A (en) * | 2021-09-30 | 2021-11-02 | 深圳市鑫道为科技有限公司 | Driving data storage method capable of extracting hit-and-run license plate information |
CN114170810A (en) * | 2021-12-28 | 2022-03-11 | 深圳市捷顺科技实业股份有限公司 | Vehicle traveling direction identification method, system and device |
CN115762143A (en) * | 2022-11-02 | 2023-03-07 | 宁波云弧科技有限公司 | Vehicle track processing method |
CN115762143B (en) * | 2022-11-02 | 2024-05-03 | 宁波云弧科技有限公司 | Vehicle track processing method |
CN116071688A (en) * | 2023-03-06 | 2023-05-05 | 台州天视智能科技有限公司 | Behavior analysis method and device for vehicle, electronic equipment and storage medium |
Also Published As
Publication number | Publication date |
---|---|
CN105632175B (en) | 2019-03-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105632175A (en) | Vehicle behavior analysis method and system | |
CN102622886B (en) | Video-based method for detecting violation lane-changing incident of vehicle | |
CN109791598A (en) | The image processing method of land mark and land mark detection system for identification | |
CN108877269B (en) | Intersection vehicle state detection and V2X broadcasting method | |
CN108227707B (en) | Automatic driving method based on laser radar and end-to-end deep learning method | |
CN105513342A (en) | Video-tracking-based vehicle queuing length calculating method | |
CN111797803A (en) | Road guardrail abnormity detection method based on artificial intelligence and image processing | |
CN112382085A (en) | System and method suitable for intelligent vehicle traffic scene understanding and beyond visual range perception | |
CN112101223B (en) | Detection method, detection device, detection equipment and computer storage medium | |
KR101678004B1 (en) | node-link based camera network monitoring system and method of monitoring the same | |
Sehestedt et al. | Robust lane detection in urban environments | |
CN102393901A (en) | Traffic flow information perception method based on hybrid characteristic and system thereof | |
CN113449632B (en) | Vision and radar perception algorithm optimization method and system based on fusion perception and automobile | |
CN107506753B (en) | Multi-vehicle tracking method for dynamic video monitoring | |
JP2016143364A (en) | Position identification equipment, position identification method, and program | |
KR20100105160A (en) | Auto transportation information extraction system and thereby method | |
CN115457780B (en) | Vehicle flow and velocity automatic measuring and calculating method and system based on priori knowledge set | |
CN113177508B (en) | Method, device and equipment for processing driving information | |
CN114972945A (en) | Multi-machine-position information fusion vehicle identification method, system, equipment and storage medium | |
CN116985873A (en) | Rail transit station track section occupation state detection method | |
CN114898553A (en) | Traffic situation monitoring method based on live-action fusion | |
KR102256205B1 (en) | Device and method for calculation vehicle position in an image, and traffic analysis system using the same | |
US20200026934A1 (en) | Image processing apparatus | |
CN109308809A (en) | A kind of tunnel device for monitoring vehicle based on dynamic image characteristic processing | |
CN113378719A (en) | Lane line recognition method and device, computer equipment and storage medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CP03 | Change of name, title or address | ||
CP03 | Change of name, title or address |
Address after: No.1 Workshop No.1, Lugong Road, Gangkou Development Zone, Fuqiao Town, Taicang City, Suzhou City, Jiangsu Province Patentee after: Suzhou Weirui Intelligent Technology Co., Ltd Address before: 200233 room 9, building 509, No. 101-40, Xuhui District, Shanghai, Caobao Road Patentee before: SHANGHAI MICROSHARP INTELLIGENT TECHNOLOGY Co.,Ltd. |