CN103021186A - Vehicle monitoring method and vehicle monitoring system - Google Patents
Vehicle monitoring method and vehicle monitoring system Download PDFInfo
- Publication number
- CN103021186A CN103021186A CN2012105846120A CN201210584612A CN103021186A CN 103021186 A CN103021186 A CN 103021186A CN 2012105846120 A CN2012105846120 A CN 2012105846120A CN 201210584612 A CN201210584612 A CN 201210584612A CN 103021186 A CN103021186 A CN 103021186A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- magnetic test
- test coil
- judge
- tracks
- 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
Images
Abstract
The invention discloses a vehicle monitoring method and a vehicle monitoring system. The method includes: detecting vehicle movement information by the aid of a detecting coil covering in a lane; tracking a vehicle entering a detecting coil area by the aid of a camera, and generating a movement track including vehicle travelling information after tracking is finished; and judging whether the vehicle violates regulations or not according to the movement track and the vehicle travelling information detected by the detecting coil. By the aid of the method, vehicles are monitored in real time, and a guarantee is provided for urban traffic safety.
Description
Technical field
The present invention relates to area of pattern recognition, relate in particular to a kind of method and system of vehicle monitoring.
Background technology
It is the main research of Intelligent Vehicle Monitoring System that the monitoring of vehicle reaches its corelation behaviour analysis in road traffic environment, also is one of the important research direction in intelligent video monitoring field.In so typical mixing in the road traffic system of China, pedestrian and vehicle have consisted of the main body of road traffic.By the intelligent video analysis technology to the vehicle movement target accurately detect, robust tracking and accurately classification, and obtain the characteristic informations such as movement locus, movement velocity and direction of target, thereby realizing such as vehicle Flow Detection and vehicle abnormality behavior detection etc., is the study hotspot of present intelligent transportation field.In conjunction with the real road traffic environment, realize accurately detection and tracking to vehicle, have important practical significance to promoting intelligent transport technology fast development and comprehensive coverage urban road traffic safety.
Although present vehicle monitoring system has much all been used video as information source, but video information is used mainly as afterwards evidence obtaining, and most of vehicle monitoring system is because hardware reason, it is installed needs to adjust according to the actual monitored scene, revise comparatively complexity, do not have general applicability.
Summary of the invention
The method and system that the purpose of this invention is to provide a kind of vehicle monitoring have realized the Real Time Monitoring of vehicle, for urban traffic safety provides guarantee.
A kind of method of vehicle monitoring, the method comprises:
Detect the vehicle movable information by the magnetic test coil that covers in the track;
Utilize video camera that the vehicle that enters this magnetic test coil zone is followed the tracks of, after following the tracks of end, generate the movement locus that comprises vehicle traveling information;
Judge according to the vehicle movement information of described movement locus and magnetic test coil detection whether vehicle is violating the regulations.
A kind of system of vehicle monitoring, this system comprises:
Magnetic test coil is used for covering the track and detects the vehicle movable information;
The trace generator module is used for utilizing video camera that the vehicle that enters this magnetic test coil zone is followed the tracks of, and after following the tracks of end, generates the movement locus that comprises vehicle traveling information;
Judge module violating the regulations is used for judging according to the vehicle movement information of described movement locus and magnetic test coil detection whether vehicle is violating the regulations.
As seen from the above technical solution provided by the invention, by magnetic test coil is set, and on the basis of automobile detecting following, finishes vehicle flowrate and violating the regulationsly judge that it is intelligent to have improved vehicle monitoring system, for urban traffic safety provides guarantee.
Description of drawings
In order to be illustrated more clearly in the technical scheme of the embodiment of the invention, the accompanying drawing of required use was done to introduce simply during the below will describe embodiment, apparently, accompanying drawing in the following describes only is some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite of not paying creative work, can also obtain other accompanying drawings according to these accompanying drawings.
The process flow diagram of the method for a kind of vehicle monitoring that Fig. 1 provides for the embodiment of the invention one;
The process flow diagram of the method for another vehicle monitoring that Fig. 2 provides for the embodiment of the invention two;
The synoptic diagram of the system of a kind of vehicle monitoring that Fig. 3 provides for the embodiment of the invention three.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on embodiments of the invention, those of ordinary skills belong to protection scope of the present invention not making the every other embodiment that obtains under the creative work prerequisite.
Embodiment one
The process flow diagram of the method for a kind of vehicle monitoring that Fig. 1 provides for the embodiment of the invention one mainly comprises the steps:
Magnetic test coil can be divided into two types: common magnetic test coil and line ball magnetic test coil.
Wherein, common magnetic test coil is quadrilateral, is tiled on a certain track, can adjust according to demand four summits of magnetic test coil and regulate its size, shape and position.Carry out the wagon flow quantitative statistics according to the vehicle of adding up by this magnetic test coil, for example, when this magnetic test coil of vehicle process, will add 1 on the vehicle fleet size through this track, magnetic test coil place.In addition, this magnetic test coil can also detect the travel direction of vehicle, for example, judges its travel direction according to same car through the order of this magnetic test coil.
The line ball magnetic test coil, it is arranged on the graticule between the track (for example, yellow line), and whether line ball travels for detection of vehicle.
By being set, above-mentioned two kinds of coils can effectively monitor vehicle running state.
Present embodiment is only processed for the video data in the magnetic test coil zone.Begin in the time of namely in vehicle enters the magnetic test coil scope to follow the tracks of, when leaving or leaving the magnetic test coil scope, stop to follow the tracks of.
With the extraction of motion information of vehicle in the magnetic test coil scope, and generate the movement locus of judging for breaking rules and regulations.
Can adopt first the Kalman filtering algorithm of based target orientation, size to carry out track filtering described movement locus is analyzed, obtain travel direction, the travel speed of vehicle and the information such as position of vehicle of vehicle;
According to the judgement of breaking rules and regulations that combines with information that magnetic test coil detects of the information such as travel direction, the travel speed of vehicle and the position of vehicle of the vehicle that obtains:
Drive in the wrong direction to judge: whether the angle of the direction that detects on the travel direction of judging described vehicle and the common magnetic test coil greater than 90 °, if be positive integer at continuous N(N, for example 10) in this angle of two field picture all greater than 90 °, judge that then this vehicle is retrograde;
Hypervelocity is judged: travel speed and the max-thresholds magnitude relationship of judging described vehicle; If the travelling speed of this vehicle all greater than max-thresholds, judges that then this overspeed of vehicle travels in continuous N two field picture;
Parking offense is judged: judge the travel speed of described vehicle and the magnitude relationship of minimum threshold; If the travelling speed of this vehicle all less than minimum threshold, judges that then this vehicle peccancy stops in continuous N two field picture;
The line ball judgement of travelling: whether the central point that frame is followed the tracks of in the position of judging this vehicle of expression enters the zone of line ball magnetic test coil, if judge that then this vehicle line ball travels.
Further, after judgement violating the regulations finishes, the colour picture of this vehicle is preserved.In addition, if judge this vehicle peccancy, then automatic alarm; For example, with this vehicle tracking frame flicker and demonstration red font, the font content is kind violating the regulations on display screen, processes to remind the staff; And with in the act of violating regulations kind of this vehicle, time violating the regulations and location information and the summary info preservation file of photochrome as this vehicle, making afterwards evidence obtaining inquire about more convenient.
The embodiment of the invention is by arranging magnetic test coil, and on the basis of automobile detecting following, finishes vehicle flowrate and violating the regulationsly judges that it is intelligent to have improved vehicle monitoring system, for urban traffic safety provides guarantee.
Embodiment two
For the ease of understanding the present invention, the present invention will be further described below in conjunction with accompanying drawing 2, as shown in Figure 2, mainly comprises the steps:
Magnetic test coil can be divided into two types: common magnetic test coil and line ball magnetic test coil.
Wherein, common magnetic test coil is quadrilateral, is tiled on a certain track, can adjust according to demand four summits of magnetic test coil and regulate its size, shape and position.Carry out the wagon flow quantitative statistics according to the vehicle of adding up by this common magnetic test coil, for example, when this magnetic test coil of vehicle process, will add 1 on the vehicle fleet size through this track, magnetic test coil place.In addition, this magnetic test coil can also detect the travel direction of vehicle, for example, judges its travel direction according to same car through the order of this common magnetic test coil.
The line ball magnetic test coil, it is arranged on the graticule between the track (for example, yellow line), and whether line ball travels for detection of vehicle.
Can effectively monitor vehicle running state by above-mentioned two kinds of coils are set, and can so that the amount of video information that system processes reduces, improve whole real-time.
Because the vehicle monitoring scene is road, the moving target in the road comprises pedestrian and vehicle, therefore, need to distinguish moving target, and extract vehicle target as monitored object.
Present embodiment adopts based on the background subtraction method and detects vehicle target; Before carrying out this algorithm, need detect first camera acquisition to image whether be the RGB(three primary colours) image, if, then can directly use, otherwise, need be converted to rgb format.The concrete steps of background subtraction method: the RGB image is carried out background modeling, obtain foreground image, detect again one or more moving targets in the foreground image, and extract the big or small target connected region profile of expression moving target; The size of the target connected region profile that obtains and the threshold value of setting are compared, if greater than this threshold value, judge that then this moving target is vehicle.
After detecting vehicle, adopt average drift Mean Shift and particle filter Particle Filter combined tracking algorithm that the vehicle that detects is followed the tracks of, have certain robustness to guarantee vehicle tracking.Track algorithm is divided into following steps:
The target connected region profile that (1) will represent vehicle is birdsed of the same feather flock together, and obtains the vehicle tabulation;
(2) utilize the Kalman Kalman filter to predict the reposition of each vehicle in the vehicle tabulation; Wherein, this Kalman wave filter iteration system equation is:
x(k)＝A×x(k-1)+B×u(k)+w(k)
z(k)＝H×x(k)+v(k)
x(k)＝x'(k)+K(k)×(z(k)-H*x'(k))
P'(k)＝A×P(k-1)×A
t+Q
K(k)＝P'(k)×H
t×inv(H×P'(k)×H
t+R)
P(k)＝(1-K(k)×H)×P'(k)；
Wherein, x' is predicted state, x is correcting state, and A is state-transition matrix, and B is gating matrix, H measures matrix, Q is noise covariance matrix, and R measures noise covariance matrix, and P' is the estimation of error covariance matrix of priori, K is the Kalman matrix, and P is posteriority estimation of error covariance matrix.
(3) carrying out vehicle collision by predicting the outcome of Kalman Kalman filter detects: if be same position in the predicting the outcome of a plurality of vehicles, then judge collision happens, then adopt Mean Shift and Particle Filter hybrid algorithm that the vehicle that bumps is followed the tracks of, and with following the tracks of the position obtain as its reposition; Otherwise, judge that vehicle does not bump, and can be divided into following two kinds of situations: if the predicting the outcome of an only corresponding vehicle of reposition, then predicting the outcome as its reposition this vehicle; If predicting the outcome of the corresponding vehicle of a plurality of repositions, then search best reposition: calculate predicting the outcome respectively according to the minimum Pasteur Bhattacharyya of histogram in Mean Shift algorithm Furthest Neighbor and obtain corresponding confidence level, the corresponding position that predicts the outcome of confidence level maximum then is the optimum position, and with the reposition of described optimum position as this vehicle; In addition, also can verify described optimum position, for example, adopt minimum distance method on the space, check whether the minimum position of distance mates with described optimum position.
Use said method to carry out vehicle tracking, after following the tracks of end, generate the movement locus that comprises vehicle heading, speed and position.
Wherein, when following situation occurring, then judges and follows the tracks of end: when continuous T (T is positive integer, for example, 3 frames) frame vehicle area all less than setting threshold, or the edge of vehicle is in beyond the image, or the size of the connected region profile of this vehicle is less than threshold value.
Can adopt the Kalman filtering algorithm of based target orientation, size to carry out track filtering, again in conjunction with the relevant information of magnetic test coil, the evaluating objects track obtains the information such as speed, travel direction and position of vehicle, for the judgement of vehicle behavior provides the most direct data.
According to the judgement of breaking rules and regulations that combines with information that magnetic test coil detects of the information such as travel direction, the travel speed of vehicle and the position of vehicle of the vehicle that obtains:
Drive in the wrong direction to judge: whether the angle of the direction that detects on the travel direction of judging described vehicle and the common magnetic test coil greater than 90 °, if be positive integer at continuous N(N, for example 10) in this angle of two field picture all greater than 90 °, judge that then this vehicle is retrograde;
Hypervelocity is judged: travel speed and the max-thresholds magnitude relationship of judging described vehicle; If the travelling speed of this vehicle all greater than max-thresholds, judges that then this overspeed of vehicle travels in continuous N two field picture;
Parking offense is judged: judge the travel speed of described vehicle and the magnitude relationship of minimum threshold; If the travelling speed of this vehicle all less than minimum threshold, judges that then this vehicle peccancy stops in continuous N two field picture;
The line ball judgement of travelling: whether the central point that frame is followed the tracks of in the position of judging this vehicle of expression enters the zone of line ball magnetic test coil, if judge that then this vehicle line ball travels.
Further, after judgement violating the regulations finishes, the colour picture of this vehicle is preserved.In addition, if judge this vehicle peccancy, then automatic alarm; For example, with this vehicle tracking frame flicker and demonstration red font, the font content is kind violating the regulations on display screen, processes to remind the staff; And with in the act of violating regulations kind of this vehicle, time violating the regulations and location information and the summary info preservation file of photochrome as this vehicle, making afterwards evidence obtaining inquire about more convenient.
The embodiment of the invention is by arranging magnetic test coil, and on the basis of automobile detecting following, finishes vehicle flowrate and violating the regulationsly judges that it is intelligent to have improved vehicle monitoring system, for urban traffic safety provides guarantee.
Through the above description of the embodiments, those skilled in the art can be well understood to above-described embodiment and can realize by software, also can realize by the mode that software adds necessary general hardware platform.Based on such understanding, the technical scheme of above-described embodiment can embody with the form of software product, it (can be CD-ROM that this software product can be stored in a non-volatile memory medium, USB flash disk, portable hard drive etc.) in, comprise some instructions with so that computer equipment (can be personal computer, server, the perhaps network equipment etc.) carry out the described method of each embodiment of the present invention.
Embodiment three
The synoptic diagram of the system of a kind of vehicle monitoring that Fig. 3 provides for the embodiment of the invention three, this system mainly comprises:
Magnetic test coil 31 is used for covering the track and detects the vehicle movable information;
Information of vehicles acquisition module 32 is used for utilizing video camera that the vehicle that enters this magnetic test coil zone is followed the tracks of, and after following the tracks of end, generates the movement locus that comprises vehicle traveling information;
Judge module 33 violating the regulations is used for judging according to the vehicle movement information of described movement locus and magnetic test coil detection whether vehicle is violating the regulations.
Described magnetic test coil 31 comprises: common magnetic test coil 311 and line ball magnetic test coil 312;
Described common magnetic test coil 311 is quadrilateral, arranges to cover on the track; Be used for obtaining the travel direction of vehicle according to detecting same vehicle by the order of this magnetic test coil, and carry out the wagon flow quantitative statistics according to the vehicle of statistics by this magnetic test coil;
Described line ball coil 312 is arranged on the graticule between the track, and whether line ball travels for detection of vehicle.
Described information of vehicles acquisition module 32 comprises:
Vehicle detection module 321 is used for detecting vehicle target based on the background subtraction method; Concrete: the image that video camera is photographed carries out background modeling, obtains foreground image, detects one or more moving targets in the foreground image, and extracts the big or small target connected region profile of expression moving target; The size of the target connected region profile that obtains and the threshold value of setting are compared, if greater than this threshold value, judge that then this moving target is vehicle;
Vehicle tracking module 322 is used for adopting average drift Mean Shift and particle filter Particle Filter combined tracking algorithm that the vehicle target that detects is followed the tracks of; Concrete: the target connected region profile that will represent vehicle is birdsed of the same feather flock together, and obtains the vehicle tabulation; Utilize the reposition of each vehicle in the tabulation of kalman Kalman filter prediction vehicle;
If be same position in the predicting the outcome of a plurality of vehicles, then judge collision happens, then adopt Mean Shift and Particle Filter hybrid algorithm that the vehicle that bumps is followed the tracks of, and with following the tracks of the position that obtains as its reposition; If predicting the outcome of an only corresponding vehicle of reposition, then predicting the outcome as its reposition this vehicle; If predicting the outcome of the corresponding vehicle of a plurality of repositions, then search best reposition: calculate predicting the outcome respectively according to the minimum Pasteur Bhattacharyya of histogram in Mean Shift algorithm Furthest Neighbor and obtain corresponding confidence level, the corresponding position that predicts the outcome of confidence level maximum then is the optimum position, and with the reposition of described optimum position as this vehicle.
Follow the tracks of and finish judge module 323, be used for judging whether following the tracks of and finish: if continuous T frame vehicle area all less than setting threshold, or the edge of vehicle is in beyond the image, or the size of the connected region profile of this vehicle is less than threshold value, judge then to follow the tracks of and finish that wherein T is positive integer;
Trace generator module 324 is used for generating the movement locus that comprises vehicle heading, speed and positional information after judging that tracking finishes.
Described judge module violating the regulations 33 comprises:
Gripper path analysis module 331 is used for described movement locus is analyzed, and the vehicle movement information that analysis result and magnetic test coil are detected combines and judges whether vehicle breaks rules and regulations; Wherein, the Kalman filtering algorithm of employing based target orientation, size carries out track filtering to be analyzed described movement locus, obtains travel direction, the travel speed of vehicle and the positional information of vehicle of vehicle.
Whether the angle of the direction that detects on the judge module 332 that drives in the wrong direction, the travel direction that is used for judging described vehicle and common magnetic test coil greater than 90 °, if in continuous this angle of N two field picture all greater than 90 °, judge that then this vehicle is retrograde, wherein, N is positive integer;
Parking offense judge module 334 is used for judging the travel speed of described vehicle and the magnitude relationship of minimum threshold; If the travelling speed of this vehicle all less than minimum threshold, judges that then this vehicle peccancy stops in continuous N two field picture;
Whether the central point that the line ball judge module 335 that travels, the position that is used for judging this vehicle of expression are followed the tracks of frame enters the zone of line ball magnetic test coil, if judge that then this vehicle line ball travels;
Described system also comprises:
The vehicle summary is preserved module 34, is used for preserving the vehicle summary info after judgement violating the regulations finishes, and this summary info comprises: the colour picture of vehicle; If this vehicle peccancy then also comprises type of violation, time violating the regulations and place violating the regulations.
Need to prove, have a detailed description among the specific implementation of the function that each processing module that comprises in the said system realizes each embodiment in front, so here repeat no more.
The those skilled in the art can be well understood to, be the convenience described and succinct, only the division with above-mentioned each functional module is illustrated, in the practical application, can as required the above-mentioned functions distribution be finished by different functional modules, the inner structure of the system of being about to is divided into different functional modules, to finish all or part of function described above.
In several embodiment that the application provides, should be understood that disclosed system and method can be realized by another way.For example, system embodiment described above only is schematic, for example, the division of described module, only be that a kind of logic function is divided, during actual the realization other dividing mode can be arranged, for example a plurality of modules can in conjunction with or can be integrated into another system, or some features can ignore, or do not carry out.Another point, the shown or coupling each other discussed or direct-coupling or communication connection can be by some interfaces, the indirect coupling of system or module or communication connection can be electrically, machinery or other form.
The above; only for the better embodiment of the present invention, but protection scope of the present invention is not limited to this, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.
Claims (10)
1. the method for a vehicle monitoring is characterized in that, the method comprises:
Detect the vehicle movable information by the magnetic test coil that covers in the track;
Utilize video camera that the vehicle that enters this magnetic test coil zone is followed the tracks of, after following the tracks of end, generate the movement locus that comprises vehicle traveling information;
Judge according to the vehicle movement information of described movement locus and magnetic test coil detection whether vehicle is violating the regulations.
2. method according to claim 1 is characterized in that, described magnetic test coil comprises: common magnetic test coil and line ball magnetic test coil;
Described common magnetic test coil is quadrilateral, is arranged on a certain track; Obtain the travel direction of vehicle according to detecting same vehicle by the order of this magnetic test coil, and carry out the wagon flow quantitative statistics according to the vehicle of statistics by this magnetic test coil;
Described line ball coil is arranged on the graticule between the track, and whether line ball travels for detection of vehicle.
3. method according to claim 1 is characterized in that, the step that described generation comprises the movement locus of vehicle traveling information comprises:
Detect vehicle target based on the background subtraction method; Concrete: the image that video camera is photographed carries out background modeling, obtains foreground image, detects one or more moving targets in the foreground image, and extracts the big or small target connected region profile of expression moving target; The size of the target connected region profile that obtains and the threshold value of setting are compared, if greater than this threshold value, judge that then this moving target is vehicle;
Adopt average drift Mean Shift and particle filter Particle Filter combined tracking algorithm that the vehicle that detects is followed the tracks of; Concrete: the target connected region profile that will represent vehicle is birdsed of the same feather flock together, and obtains the vehicle tabulation; Utilize the reposition of each vehicle in the tabulation of Kalman Kalman filter prediction vehicle;
If be same position in the predicting the outcome of a plurality of vehicles, then judge collision happens, then adopt mean shift and Particle Filter hybrid algorithm that the vehicle that bumps is followed the tracks of, and with following the tracks of the position that obtains as its reposition; If predicting the outcome of an only corresponding vehicle of reposition, then predicting the outcome as its reposition this vehicle; If predicting the outcome of the corresponding vehicle of a plurality of repositions, then search best reposition: calculate predicting the outcome respectively according to the minimum Pasteur Bhattacharyya of histogram in Mean Shift algorithm Furthest Neighbor and obtain corresponding confidence level, the corresponding position that predicts the outcome of confidence level maximum then is the optimum position, and with the reposition of described optimum position as this vehicle;
Follow the tracks of to finish judge: if continuous T frame vehicle area all less than setting threshold, or the edge of vehicle is in beyond the image, or the size of the connected region profile of this vehicle judges then to follow the tracks of finish that less than threshold value wherein T is positive integer;
After judging that tracking finishes, generate the movement locus that comprises vehicle heading, speed and positional information.
4. according to claim 1,2 or 3 described methods, it is characterized in that described movement locus and the vehicle movement information that detects of magnetic test coil according to generating judges that the step whether vehicle breaks rules and regulations comprises:
The Kalman filtering algorithm of employing based target orientation, size carries out track filtering to be analyzed described movement locus, obtains travel direction, the travel speed of vehicle and the positional information of vehicle of vehicle;
Combine to judge whether vehicle is violating the regulations with obtaining the travel direction of vehicle, the travel speed of vehicle and the positional information of vehicle with the vehicle movement information that magnetic test coil detects:
Whether judge the angle of the direction that detects on the travel direction of described vehicle and the common magnetic test coil greater than 90 °, if in continuous this angle of N two field picture all greater than 90 °, judge that then this vehicle drives in the wrong direction, wherein, N is positive integer;
Judge the travel speed of described vehicle and the magnitude relationship of max-thresholds; If the travelling speed of this vehicle all greater than max-thresholds, judges that then this overspeed of vehicle travels in continuous N two field picture;
Judge the travel speed of described vehicle and the magnitude relationship of minimum threshold; If the travelling speed of this vehicle all less than minimum threshold, judges that then this vehicle peccancy stops in continuous N two field picture;
Judge whether the central point of the position tracking frame of this vehicle of expression enters the zone of line ball magnetic test coil, if judge that then this vehicle line ball travels.
5. each described method according to claim 1-4 is characterized in that the method also comprises:
Preserve the vehicle summary info after judgement violating the regulations finishes, this summary info comprises: the colour picture of vehicle; If this vehicle peccancy then also comprises type of violation, time violating the regulations and place violating the regulations.
6. the system of a vehicle monitoring is characterized in that, this system comprises:
Magnetic test coil is used for covering the track and detects the vehicle movable information;
The information of vehicles acquisition module is used for utilizing video camera that the vehicle that enters this magnetic test coil zone is followed the tracks of, and after following the tracks of end, generates the movement locus that comprises vehicle traveling information;
Judge module violating the regulations is used for judging according to the vehicle movement information of described movement locus and magnetic test coil detection whether vehicle is violating the regulations.
7. system according to claim 6 is characterized in that, described magnetic test coil comprises: common magnetic test coil and line ball magnetic test coil;
Described common magnetic test coil is quadrilateral, is arranged on a certain track; Be used for obtaining the travel direction of vehicle according to detecting same vehicle by the order of this magnetic test coil, and carry out the wagon flow quantitative statistics according to the vehicle of statistics by this magnetic test coil;
Described line ball coil is arranged on the graticule between the track, and whether line ball travels for detection of vehicle.
8. system according to claim 6 is characterized in that, described information of vehicles acquisition module comprises:
The vehicle detection module is used for detecting vehicle target based on the background subtraction method; Concrete: the image that video camera is photographed carries out background modeling, obtains foreground image, detects one or more moving targets in the foreground image, and extracts the big or small target connected region profile of expression moving target; The size of the target connected region profile that obtains and the threshold value of setting are compared, if greater than this threshold value, judge that then this moving target is vehicle;
The vehicle tracking module is used for adopting average drift Mean Shift and particle filter Particle Filter combined tracking algorithm that the vehicle target that detects is followed the tracks of; Concrete: the target connected region profile that will represent vehicle is birdsed of the same feather flock together, and obtains the vehicle tabulation; Utilize the reposition of each vehicle in the tabulation of Kalman Kalman filter prediction vehicle;
If be same position in the predicting the outcome of a plurality of vehicles, then judge collision happens, then adopt Mean Shift and Particle Filter hybrid algorithm that the vehicle that bumps is followed the tracks of, and with following the tracks of the position that obtains as its reposition; If predicting the outcome of an only corresponding vehicle of reposition, then predicting the outcome as its reposition this vehicle; If predicting the outcome of the corresponding vehicle of a plurality of repositions, then search best reposition: calculate predicting the outcome respectively according to the minimum Pasteur Bhattacharyya of histogram in Mean Shift algorithm Furthest Neighbor and obtain corresponding confidence level, the corresponding position that predicts the outcome of confidence level maximum then is the optimum position, and with the reposition of described optimum position as this vehicle.
Follow the tracks of and finish judge module, be used for judging whether following the tracks of and finish: if continuous T frame vehicle area all less than setting threshold, or the edge of vehicle is in beyond the image, or the size of the connected region profile of this vehicle is less than threshold value, judge then to follow the tracks of and finish that wherein T is positive integer;
The trace generator module is used for generating the movement locus that comprises vehicle heading, speed and positional information after judging that tracking finishes.
9. according to claim 6,7 or 8 described systems, it is characterized in that described judge module violating the regulations comprises:
The gripper path analysis module, the Kalman filtering algorithm that is used for employing based target orientation, size carries out track filtering to be analyzed described movement locus, obtains travel direction, the travel speed of vehicle and the positional information of vehicle of vehicle;
Whether the angle of the direction that detects on the judge module that drives in the wrong direction, the travel direction that is used for judging described vehicle and common magnetic test coil greater than 90 °, if in continuous this angle of N two field picture all greater than 90 °, judge that then this vehicle is retrograde, wherein, N is positive integer;
The hypervelocity judge module is used for judging the travel speed of described vehicle and the magnitude relationship of max-thresholds; If the travelling speed of this vehicle all greater than max-thresholds, judges that then this overspeed of vehicle travels in continuous N two field picture;
The parking offense judge module is used for judging the travel speed of described vehicle and the magnitude relationship of minimum threshold; If the travelling speed of this vehicle all less than minimum threshold, judges that then this vehicle peccancy stops in continuous N two field picture;
Whether the central point that the line ball judge module that travels, the position that is used for judging this vehicle of expression are followed the tracks of frame enters the zone of line ball magnetic test coil, if judge that then this vehicle line ball travels.
10. each described system is characterized in that according to claim 6-9, and described system also comprises:
The vehicle summary is preserved module, is used for preserving the vehicle summary info after judgement violating the regulations finishes, and this summary info comprises: the colour picture of vehicle; If this vehicle peccancy then also comprises type of violation, time violating the regulations and place violating the regulations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210584612.0A CN103021186B (en) | 2012-12-28 | 2012-12-28 | Vehicle monitoring method and vehicle monitoring system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210584612.0A CN103021186B (en) | 2012-12-28 | 2012-12-28 | Vehicle monitoring method and vehicle monitoring system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103021186A true CN103021186A (en) | 2013-04-03 |
| CN103021186B CN103021186B (en) | 2015-03-25 |
Family
ID=47969741
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210584612.0A Active CN103021186B (en) | 2012-12-28 | 2012-12-28 | Vehicle monitoring method and vehicle monitoring system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103021186B (en) |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134222A (en) * | 2014-07-09 | 2014-11-05 | 郑州大学 | Traffic flow monitoring image detecting and tracking system and method based on multi-feature fusion |
| CN105721826A (en) * | 2014-12-02 | 2016-06-29 | 四川浩特通信有限公司 | Intelligent combat system |
| CN106412508A (en) * | 2016-09-30 | 2017-02-15 | 北京中星微电子有限公司 | Intelligent monitoring method and system of illegal line press of vehicles |
| CN106558224A (en) * | 2015-09-30 | 2017-04-05 | 徐贵力 | A kind of traffic intelligent monitoring and managing method based on computer vision |
| CN106651911A (en) * | 2016-11-17 | 2017-05-10 | 天津大学 | Fish behavior acquiring method based on image tracking |
| CN106709476A (en) * | 2017-01-24 | 2017-05-24 | 福州市规划设计研究院 | Intersection OD investigation method |
| CN107657628A (en) * | 2017-09-20 | 2018-02-02 | 中国科学院长春光学精密机械与物理研究所 | A kind of real-time color method for tracking target |
| CN108229407A (en) * | 2018-01-11 | 2018-06-29 | 武汉米人科技有限公司 | A kind of behavioral value method and system in video analysis |
| CN108256501A (en) * | 2018-02-05 | 2018-07-06 | 李刚毅 | Abnormal motion object detection systems and its method |
| CN108376407A (en) * | 2018-02-05 | 2018-08-07 | 李刚毅 | Hot-zone object aggregation detection method and system |
| CN108388845A (en) * | 2018-02-05 | 2018-08-10 | 李刚毅 | Method for checking object and system |
| CN108417050A (en) * | 2018-03-02 | 2018-08-17 | 西南交通大学 | A kind of excess speed event real-time detecting method based on continuous hypervelocity GPS track point |
| CN108492523A (en) * | 2018-05-21 | 2018-09-04 | 四川斐讯信息技术有限公司 | A kind of safety protecting method and system based on intelligent wearable device |
| CN108693532A (en) * | 2018-03-29 | 2018-10-23 | 浙江大学 | Wearable barrier-avoiding method and device based on enhanced binocular camera Yu 3D millimetre-wave radars |
| CN109313847A (en) * | 2016-06-07 | 2019-02-05 | 罗伯特·博世有限公司 | Method, apparatus and system for wrong road driver identification |
| CN109313850A (en) * | 2016-06-07 | 2019-02-05 | 罗伯特·博世有限公司 | Method, apparatus and system for driver's identification of driving in the wrong direction |
| 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 |
| CN110032947A (en) * | 2019-03-22 | 2019-07-19 | 深兰科技（上海）有限公司 | A kind of method and device that monitor event occurs |
| CN110910654A (en) * | 2019-12-03 | 2020-03-24 | 上海眼控科技股份有限公司 | Illegal information processing method and device, electronic equipment and readable storage medium |
| CN110969864A (en) * | 2018-09-28 | 2020-04-07 | 杭州海康威视数字技术股份有限公司 | Vehicle speed detection method, vehicle driving event detection method and electronic equipment |
| CN111047874A (en) * | 2019-12-19 | 2020-04-21 | 中科寒武纪科技股份有限公司 | Intelligent traffic violation management method and related product |
| CN112424636A (en) * | 2018-07-20 | 2021-02-26 | 株式会社电装 | Moving object detection device |
| CN113257033A (en) * | 2021-07-01 | 2021-08-13 | 成都宜泊信息科技有限公司 | Parking lot management method and system, storage medium and electronic equipment |
| CN114360257A (en) * | 2022-01-07 | 2022-04-15 | 重庆紫光华山智安科技有限公司 | Vehicle monitoring method and system |
| CN116740126A (en) * | 2023-08-09 | 2023-09-12 | 深圳市深视智能科技有限公司 | Target tracking method, high-speed camera, and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6690292B1 (en) * | 2000-06-06 | 2004-02-10 | Bellsouth Intellectual Property Corporation | Method and system for monitoring vehicular traffic using a wireless communications network |
| CN1831892A (en) * | 2006-04-18 | 2006-09-13 | 北京铱星世纪数字应用开发有限责任公司 | Traffic monitoring method and electronic police system |
| JP2008152736A (en) * | 2006-12-20 | 2008-07-03 | Sony Corp | Monitoring system, monitoring device, and monitoring method |
| CN101556737A (en) * | 2008-04-10 | 2009-10-14 | 上海宝康电子控制工程有限公司 | Left-turn violation detection system at vehicle intersection |
| CN101572770A (en) * | 2009-06-18 | 2009-11-04 | 中国科学技术大学 | Method for testing motion available for real-time monitoring and device thereof |
| CN101572803A (en) * | 2009-06-18 | 2009-11-04 | 中国科学技术大学 | Customizable automatic tracking system based on video monitoring |
| CN201438334U (en) * | 2009-06-02 | 2010-04-14 | 东莞市大一软件科技有限公司 | Red light running electronic police system |
| CN201535951U (en) * | 2009-09-23 | 2010-07-28 | 德瑞视(北京)科技发展有限公司 | Intelligent traffic integrated video monitoring system |
| CN102298844A (en) * | 2011-08-15 | 2011-12-28 | 无锡中星微电子有限公司 | Automatic rule breaking vehicle detection system and method |
-
2012
- 2012-12-28 CN CN201210584612.0A patent/CN103021186B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6690292B1 (en) * | 2000-06-06 | 2004-02-10 | Bellsouth Intellectual Property Corporation | Method and system for monitoring vehicular traffic using a wireless communications network |
| CN1831892A (en) * | 2006-04-18 | 2006-09-13 | 北京铱星世纪数字应用开发有限责任公司 | Traffic monitoring method and electronic police system |
| JP2008152736A (en) * | 2006-12-20 | 2008-07-03 | Sony Corp | Monitoring system, monitoring device, and monitoring method |
| CN101556737A (en) * | 2008-04-10 | 2009-10-14 | 上海宝康电子控制工程有限公司 | Left-turn violation detection system at vehicle intersection |
| CN201438334U (en) * | 2009-06-02 | 2010-04-14 | 东莞市大一软件科技有限公司 | Red light running electronic police system |
| CN101572770A (en) * | 2009-06-18 | 2009-11-04 | 中国科学技术大学 | Method for testing motion available for real-time monitoring and device thereof |
| CN101572803A (en) * | 2009-06-18 | 2009-11-04 | 中国科学技术大学 | Customizable automatic tracking system based on video monitoring |
| CN201535951U (en) * | 2009-09-23 | 2010-07-28 | 德瑞视(北京)科技发展有限公司 | Intelligent traffic integrated video monitoring system |
| CN102298844A (en) * | 2011-08-15 | 2011-12-28 | 无锡中星微电子有限公司 | Automatic rule breaking vehicle detection system and method |
Non-Patent Citations (2)
| Title |
|---|
| 朱明清 等: "基于人类视觉智能和粒子滤波的鲁棒目标跟踪算法", 《控制与决策》 * |
| 朱明清 等: "基于自适应过程噪声分布的粒子滤波视觉目标跟踪方法", 《2010系统仿真技术及其应用学术会议论文集》 * |
Cited By (31)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134222B (en) * | 2014-07-09 | 2017-02-15 | 郑州大学 | Traffic flow monitoring image detecting and tracking system and method based on multi-feature fusion |
| CN104134222A (en) * | 2014-07-09 | 2014-11-05 | 郑州大学 | Traffic flow monitoring image detecting and tracking system and method based on multi-feature fusion |
| CN105721826B (en) * | 2014-12-02 | 2018-06-12 | 四川浩特通信有限公司 | A kind of intelligence actual combat system |
| CN105721826A (en) * | 2014-12-02 | 2016-06-29 | 四川浩特通信有限公司 | Intelligent combat system |
| CN106558224A (en) * | 2015-09-30 | 2017-04-05 | 徐贵力 | A kind of traffic intelligent monitoring and managing method based on computer vision |
| CN106558224B (en) * | 2015-09-30 | 2019-08-02 | 徐贵力 | A kind of traffic intelligent monitoring and managing method based on computer vision |
| CN109313847A (en) * | 2016-06-07 | 2019-02-05 | 罗伯特·博世有限公司 | Method, apparatus and system for wrong road driver identification |
| US11315417B2 (en) | 2016-06-07 | 2022-04-26 | Robert Bosch Gmbh | Method, device and system for wrong-way driver detection |
| CN109313850A (en) * | 2016-06-07 | 2019-02-05 | 罗伯特·博世有限公司 | Method, apparatus and system for driver's identification of driving in the wrong direction |
| CN106412508A (en) * | 2016-09-30 | 2017-02-15 | 北京中星微电子有限公司 | Intelligent monitoring method and system of illegal line press of vehicles |
| CN106651911A (en) * | 2016-11-17 | 2017-05-10 | 天津大学 | Fish behavior acquiring method based on image tracking |
| CN106709476A (en) * | 2017-01-24 | 2017-05-24 | 福州市规划设计研究院 | Intersection OD investigation method |
| CN107657628A (en) * | 2017-09-20 | 2018-02-02 | 中国科学院长春光学精密机械与物理研究所 | A kind of real-time color method for tracking target |
| CN108229407A (en) * | 2018-01-11 | 2018-06-29 | 武汉米人科技有限公司 | A kind of behavioral value method and system in video analysis |
| CN108256501A (en) * | 2018-02-05 | 2018-07-06 | 李刚毅 | Abnormal motion object detection systems and its method |
| CN108376407A (en) * | 2018-02-05 | 2018-08-07 | 李刚毅 | Hot-zone object aggregation detection method and system |
| CN108388845A (en) * | 2018-02-05 | 2018-08-10 | 李刚毅 | Method for checking object and system |
| CN108417050A (en) * | 2018-03-02 | 2018-08-17 | 西南交通大学 | A kind of excess speed event real-time detecting method based on continuous hypervelocity GPS track point |
| CN108693532A (en) * | 2018-03-29 | 2018-10-23 | 浙江大学 | Wearable barrier-avoiding method and device based on enhanced binocular camera Yu 3D millimetre-wave radars |
| CN108492523A (en) * | 2018-05-21 | 2018-09-04 | 四川斐讯信息技术有限公司 | A kind of safety protecting method and system based on intelligent wearable device |
| CN112424636A (en) * | 2018-07-20 | 2021-02-26 | 株式会社电装 | Moving object detection device |
| CN110969864A (en) * | 2018-09-28 | 2020-04-07 | 杭州海康威视数字技术股份有限公司 | Vehicle speed detection method, vehicle driving event detection method and electronic equipment |
| 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 |
| CN110032947A (en) * | 2019-03-22 | 2019-07-19 | 深兰科技（上海）有限公司 | A kind of method and device that monitor event occurs |
| CN110032947B (en) * | 2019-03-22 | 2021-11-19 | 深兰科技（上海）有限公司 | Method and device for monitoring occurrence of event |
| CN110910654A (en) * | 2019-12-03 | 2020-03-24 | 上海眼控科技股份有限公司 | Illegal information processing method and device, electronic equipment and readable storage medium |
| CN111047874A (en) * | 2019-12-19 | 2020-04-21 | 中科寒武纪科技股份有限公司 | Intelligent traffic violation management method and related product |
| CN113257033A (en) * | 2021-07-01 | 2021-08-13 | 成都宜泊信息科技有限公司 | Parking lot management method and system, storage medium and electronic equipment |
| CN114360257A (en) * | 2022-01-07 | 2022-04-15 | 重庆紫光华山智安科技有限公司 | Vehicle monitoring method and system |
| CN114360257B (en) * | 2022-01-07 | 2023-02-28 | 重庆紫光华山智安科技有限公司 | Vehicle monitoring method and system |
| CN116740126A (en) * | 2023-08-09 | 2023-09-12 | 深圳市深视智能科技有限公司 | Target tracking method, high-speed camera, and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103021186B (en) | 2015-03-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103021186B (en) | Vehicle monitoring method and vehicle monitoring system | |
| CN103971521B (en) | Road traffic anomalous event real-time detection method and device | |
| CN102622884B (en) | Vehicle illegal turning behavior detection method based on tracking | |
| Mukojima et al. | Moving camera background-subtraction for obstacle detection on railway tracks | |
| CN102810250B (en) | Video based multi-vehicle traffic information detection method | |
| CN105825185B (en) | Vehicle collision avoidance method for early warning and device | |
| CN102881022B (en) | Concealed-target tracking method based on on-line learning | |
| Unzueta et al. | Adaptive multicue background subtraction for robust vehicle counting and classification | |
| CN104537841B (en) | Unlicensed vehicle violation detection method and detection system thereof | |
| CN110371112A (en) | A kind of intelligent barrier avoiding system and method for automatic driving vehicle | |
| KR100969995B1 (en) | System of traffic conflict decision for signalized intersections using image processing technique | |
| CN105513349B (en) | Mountainous area highway vehicular events detection method based on double-visual angle study | |
| US10699567B2 (en) | Method of controlling a traffic surveillance system | |
| CN106652465A (en) | Method and system for identifying abnormal driving behavior on road | |
| CN105761494B (en) | Abnormal traffic information collecting method based on unmanned plane | |
| CN105632186A (en) | Method and device for detecting vehicle queue jumping behavior | |
| Chen et al. | Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences | |
| CN105744232A (en) | Method for preventing power transmission line from being externally broken through video based on behaviour analysis technology | |
| CN102568206B (en) | Video monitoring-based method for detecting cars parking against regulations | |
| CN103150740A (en) | Method and system for moving target tracking based on video | |
| CN103345618A (en) | Traffic violation detection method based on video technology | |
| CN110047276A (en) | The congestion status of barrier vehicle determines method, apparatus and Related product | |
| CN105575125A (en) | Vehicle flow video detection and analysis system | |
| CN105931467A (en) | Target tracking method and device | |
| CN105574502A (en) | Automatic detection method for violation behaviors of self-service card sender |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |