Research On Segmentation And Tracking Of Adhesions Vehicle In The Video

In the traffic video sequence, the angle between the optical axis of the camera and theroad plane tend to be small, and off the center road often. So it appears adhesion betweenmultiple vehicles in the output image, what will cause many problems in vehicle tracking.This paper focuses on adhesions vehicle segmentation and tracking method.Image preprocessing includes interest lane area extraction and coordinate systemconversion, resulting in dimensional coordinate system in the real world. Then after Gaussianmixture model and background subtraction we can obtain the prospects vehicles inside thelane. The complete and smooth prospects of vehicles are obtained through the technique ofholds filling and morphological processing.In adhesions vehicle segmentation, extracted the skeleton structure of prospects simplyconnected regions. Detect the number of occluded vehicles in one blob and the segmentationpoints based on the position and number of effective skeleton nodes, and then segmentoccluded vehicles in one blob associated with concave spots.In vehicle tracking, using the feature points matching algorithm. Therefore, we use theHarris corner detector to detect feature points. To reduce the effects of shadows, we alsoignore any feature that lies within a small distanceτ sfrom a background pixel. By utilizinga3-D perspective mapping from the scene to the image, along with a plumb line projection,we are able to distinguish a subset of stable features whose3-D coordinates can be accuratelyestimated. These features are then grouped based on the number and locations of the vehicles.To measure similarity on the stable features to identify the matching feature points.Associate the gray feature descriptor with cross-correlation coefficient to achieve featurepoints matching. Remove the error matching feature points based on the geometriccharacteristics. Kalman prediction is applied to the features matching, and the position ofvehicle and features are predicted in the next frame. Finally design a multi-vehicle trackingsystem to obtain the locations of the vehicles over time.