CN102222346A - Vehicle detecting and tracking method - Google Patents
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Abstract
The invention relates to a vehicle detecting and tracking method. The method is characterized by comprising the following steps of: establishing a Gaussian background model for each frame image in a video; performing differential processing on two adjacent frames with a frame subtraction method to obtain a rough motion region and a rough static region; performing background updating on the obtained static region and not updating the motion region; differentiating a current frame image and an updated background image to obtain an accurate motion region; finding an overlap region between motion region images of two obtained adjacent frames with a pixel point matching method and comparing the overlap region with a given threshold value; if the overlap region is larger than the given threshold value, judging whether target overlap occurs; if so, calculating the length-width ratio of a first frame motion region in two adjacent frames and detecting and tracking a motion vehicle according to the ratio; otherwise, judging that the same vehicle moves; and if the overlap region is smaller than the given threshold value, evaluating a minimum external rectangle of a plurality of target frames to correctly detect and track the vehicle.
Description
Technical field
The present invention relates to the Flame Image Process aspect, particularly a kind of vehicle detection and tracking.
Background technology
Intelligent transportation system (ITS) is a big focus of researching and developing at present.Intelligent transportation system is to apply to whole traffic management system with advanced person's infotech, data communication transmission technology, electronic sensor technology, electron controls technology and Computer Processing technology etc. are integrated effectively, and set up a kind of on a large scale in, comprehensive playing a role, in real time, comprehensive transportation and management system accurately and efficiently.
Wherein, in intelligent transportation system, vehicle detection and tracking are most basic parts, it requires from the resulting image sequence of video camera, detection has or not moving vehicle to enter the examination hall of video camera, and the position of setting movement vehicle, and it belongs to the research range of computer vision.
At present, have powerful connections poor method, frame difference method and optical flow method of Chang Yong vehicle checking method.Wherein, the background subtraction method is a kind of method the most frequently used during present moving vehicle is cut apart, and it is to utilize the difference of present image and image to detect a kind of technology of moving region.It utilizes mixed Gaussian distribution modeling to each pixel, and utilizes On-line Estimation to upgrade based on the adaptive Gaussian Background model that closes, thereby has handled the influences such as interference that illumination variation, background clutter are moved reliably.This method can be extracted complete characteristic, but it is for the variation of dynamic scene, and is very responsive as weather, illumination and trees disturbance.Therefore, it is crucial how obtaining a background with real-time update ability.Frame difference method is to do to extract the detection that moving vehicle is realized in the moving region based on the time difference and the thresholding of pixel between two frames fixing in the continuous images sequence or the multiframe partition image.Lipton etc. utilize two frame difference methods to detect moving target from real video, and then are used for identification of targets and tracking; Frame difference method has stronger adaptivity for dynamic environment, but generally can not extract all relevant feature pixels fully.In vehicle target inside, be easy to generate cavitation, and general detected vehicle target can be elongated all, be difficult to obtain more accurate target area.Moving target changes in time and has the light stream characteristic, therefore just has the optical flow field of the target utilized to realize motion detection.1981, Horn and Schunck creatively interrelated two-dimension speed field and gray scale, introduced the optical flow constraint equation, obtained the rudimentary algorithm of optical flow computation.People such as Barron sum up multiple optical flow computation technology, according to the difference of theoretical foundation and mathematical method they are divided into four kinds: based on the method for gradient, based on the method for coupling, based on the method for energy, based on the method for phase place.But most optical flow computation methods is quite complicated, and noise resisting ability is poor, must have specific hardware supported just can finish when handling real-time full frame video stream, otherwise can't satisfy the requirement of real-time.
Will follow the tracks of the moving vehicle that detects, vehicle tracking algorithm at present commonly used mainly contains: based on the track algorithm of feature, based on the track algorithm of 3-D, based on the track algorithm of distorted pattern with based on the track algorithm in zone.Wherein, based on the track algorithm of feature, exactly each car is extracted some features such as diacritic straight line or turning etc., or these characteristics combination are represented a vehicle, even the outstanding advantage of this class algorithm is to have partial occlusion, some features are still visible.But when vehicle each other too near the time, the problem that exists feature too closely can't cut apart.Based on the track algorithm of 3-D is by using the geometry knowledge of video camera and scene, has the three-dimensional model of precise geometry to project into image with one, follows the tracks of according to the change in location in the image.The advantage of this class algorithm is an accuracy rate height when type of vehicle of determining and geometric model details, and shortcoming is that real-time is poor because the workload of calculating is big.Track algorithm based on distorted pattern is tracing object with the vehicle ' s contour, extracts contour feature by the snake active contour model.This method is blocked sensitivity to noise, has profile initialization problem.Based on the track algorithm in zone at first join domain extract and according to circumstances merged or cut apart.The most serious weakness of this method is that the zone merges and cut apart the inaccurate situation that exists in vehicle detection.
More than multiple vehicle detection and to follow the tracks of efficient not high, need to be badly in need of addressing this problem.
Summary of the invention
The objective of the invention is to, for addressing the above problem, the present invention proposes a kind of vehicle detection and tracking, reaches the efficient that has improved detection and tracking.
For achieving the above object, the present invention proposes a kind of vehicle detection and tracking, it is characterized in that, this method step comprises:
Step 1): each two field picture in the video is set up the Gaussian Background model;
Step 2): this background model image of each vertical frame dimension according to described step 1) obtains, utilize frame difference method that adjacent two frames are done difference processing, obtain rough moving region and stagnant zone;
Step 3): to described step 2) stagnant zone that obtains in carries out context update, and the moving region is not upgraded;
Step 4): the background image after the renewal of current frame image and the acquisition of described step 3) is done difference, obtain the accurate movement zone;
Step 5): utilize each pixel matching process that the adjacent two frame moving region images that described step 4) obtains are found out the overlapping region, and compare overlapping region and given threshold size;
If greater than given threshold value, then judging whether to take place target, the overlapping region overlaps; If then calculate the length breadth ratio of the first frame moving region in adjacent two frames, by this this moving vehicle of ratio detection and tracking; If not, then be judged as same vehicle;
If, then obtaining the minimum boundary rectangle of a plurality of target frames less than given threshold value, the overlapping region comes correctly to vehicle detection and tracking.
Threshold value in the described step 5) is 50% of target frame zone.
The invention has the advantages that the present invention combines background subtraction method and frame difference method, overcome the deficiency of two methods, can determine background more accurately and be partitioned into the moving region.Simultaneously,, judge, overcome shortcoming, obtained good effect based on the track algorithm in zone for overlapping vehicle and division zone at target following and matching stage.
Description of drawings
Fig. 1 is a kind of vehicle detection of the present invention and tracking process flow diagram;
Fig. 2 is a background subtraction method synoptic diagram;
Fig. 3 is the overlapping synoptic diagram of moving target;
Fig. 4 is the division synoptic diagram of same vehicle target area;
Fig. 5 is the fusion synoptic diagram in division zone.
Embodiment
Below in conjunction with the drawings and specific embodiments, method of the present invention is described in more detail.
As shown in Figure 1, Fig. 1 is a kind of vehicle detection of the present invention and tracking process flow diagram.Improved vehicle detection and track algorithm that the present invention proposes are described in detail as follows in conjunction with the embodiments: the present embodiment method may further comprise the steps:
1), sets up the Gaussian Background model to each two field picture;
Single Gaussian Background model that distributes thinks that to a background image, the specific pixel lightness distribution satisfies Gaussian distribution, and each pixel property of background model comprises two parameters like this: mean value u and variance d.Set up the process of background model and exactly each pixel is obtained this two parameters.
2) utilize frame difference method, find out rough moving region and stagnant zone;
Adjacent two frames are done difference processing, obtain the zone of motion change.Find out the edge of moving object again by edge extracting, detect moving object at last.
3) to 2) in the stagnant zone found out carry out context update, the moving region is not upgraded;
Along with the variation of time, background image also can take place to change slowly, and at this moment we will bring in constant renewal in the mean value of each picture element:
u(t+1，x，y)＝a*u(t，x，y)+(1-a)*I(x，y) (1)
4) utilize the background subtraction method, find out the accurate moving region of present frame;
As shown in Figure 2, Fig. 2 is a background subtraction method synoptic diagram.Background image after current frame image and the renewal is done difference, obtain the moving region.For the moving region of consecutive frame, seek out lap; Promptly, utilize the method for each pixel coupling, find out lap for adjacent two two field pictures.
5) overlap the judgement of vehicle and cutting apart;
As shown in Figure 3, Fig. 3 is the overlapping synoptic diagram of moving target.When vehicle distances is too near, will move the region overlapping phenomenon.When tracked vehicle has taken place overlappingly with other vehicle in next frame, with existing track algorithm the omission phenomenon will take place directly, follow the tracks of again after the dividing processing below therefore after following the tracks of failure for the first time, carrying out.
Suppose that the detection of i frame moving region is correct, the i+1 frame target occurs and overlaps.So, in the overlapping region was judged, it was overlapping just to have 2 zones and i+1 red area, promptly existed target to overlap.Obtain the length breadth ratio of i frame moving region, be partitioned into moving vehicle by this ratio.
6) judgement and the fusion in same vehicle division zone;
As shown in Figure 4, Fig. 4 is the division synoptic diagram of same vehicle target area.When vehicle bigger, bus for example because low running speed or texture are close with background, the moving region of obtaining splits into a plurality of zones through overexpansion corrosion operation through regular meeting, this will cause mistake to mate.
Bearing calibration: the overlapping region of calculating each target frame of i frame and i-1 frame target frame.If the overlapping region, is just thought coupling greater than 50% of target frame zone.If the same frame coupling of a plurality of target frames of i frame and i-1 frame is just thought to have the mistake coupling, need merge.As shown in Figure 5, Fig. 5 is the fusion synoptic diagram in division zone.The method that merges is to ask the minimum boundary rectangle of these target frames.
Improved vehicle detection and track algorithm are tested in real system.With the algorithm among the present invention vehicle is detected tracking and counting, count results is compared with the artificial counting result.The actual travel vehicle is 260, improves the vehicle numerical digit 261 that the back algorithm computation obtains; With background subtraction method and the vehicle numerical digit 238 that obtains based on the tracking in zone; With frame difference method and the vehicle numerical digit 241 that obtains based on the tracking in zone.As seen, the method accuracy rate after the improvement has had significantly raising.
It should be noted last that above embodiment is only unrestricted in order to technical scheme of the present invention to be described.Although the present invention is had been described in detail with reference to embodiment, those of ordinary skill in the art is to be understood that, technical scheme of the present invention is made amendment or is equal to replacement, do not break away from the spirit and scope of technical solution of the present invention, it all should be encompassed in the middle of the claim scope of the present invention.
Claims (2)
1. vehicle detection and tracking is characterized in that this method step comprises:
Step 1): each two field picture in the video is set up the Gaussian Background model;
Step 2): this background model image of each vertical frame dimension according to described step 1) obtains, utilize frame difference method that adjacent two frames are done difference processing, obtain rough moving region and stagnant zone;
Step 3): to described step 2) stagnant zone that obtains in carries out context update, and the moving region is not upgraded;
Step 4): the background image after the renewal of current frame image and the acquisition of described step 3) is done difference, obtain the accurate movement zone;
Step 5): utilize each pixel matching process that the adjacent two frame moving region images that described step 4) obtains are found out the overlapping region, and compare overlapping region and given threshold size;
If greater than given threshold value, then judging whether to take place target, the overlapping region overlaps; If then calculate the length breadth ratio of the first frame moving region in adjacent two frames, by this this moving vehicle of ratio detection and tracking; If not, then be judged as same vehicle;
If, then obtaining the minimum boundary rectangle of a plurality of target frames less than given threshold value, the overlapping region comes correctly to vehicle detection and tracking.
2. vehicle detection according to claim 1 and tracking is characterized in that, the threshold value in the described step 5) is 50% of target frame zone.
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| CN103116986A (en) * | 2013-01-21 | 2013-05-22 | 信帧电子技术（北京）有限公司 | Vehicle identification method |
| US20130286205A1 (en) * | 2012-04-27 | 2013-10-31 | Fujitsu Limited | Approaching object detection device and method for detecting approaching objects |
| CN103425764B (en) * | 2013-07-30 | 2017-04-12 | 广东工业大学 | Vehicle matching method based on videos |
| CN108010050A (en) * | 2017-11-27 | 2018-05-08 | 电子科技大学 | A kind of foreground detection method based on adaptive RTS threshold adjustment and selective context update |
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