Vision-based Moving Object Detection&Tracking Technology Research

In this paper, moving object tracking in complex background is as the research keypoint. Object tacking algorithm based on object feature and multi-camera coordinationobject tracking based on object model were thorough studied. In detail, moving objectdetection and tracking technology has got a preliminary study. On the base of objectfeatures based on SURF algorithm, regional prediction method and miss matchdetection mechanism are used to improve a single object tacking algorithm whichcombines with characteristics of SURF and Kalman filtering. The improve algorithmhas obtained a steady tracking effect. Furthermore, the improved object trackingalgorithm is used in the multi-camera coordination object tracking. Some object modelsbased on SURF feature are used to realize the object handover of multi-cameracoordination object tracking. At last, the experiment proved that the object has obtainedsuccessful handover and steady tracking based on the improved methods. In this paper，the main researches are as follows：①In this paper, the object detection methods based on feature points have studied.Harris algorithm is the traditional good feature points detection algorithm. At the sametime, as the emerging local invariant feature extraction method, SIFT algorithm iswidely used. Furthermore, SURF as the speed-up method of SIFT is compared withHarris, SIFT. A appropriate algorithm is employed as the next improved object trackingalgorithm with the compromise between extract speed and detection precision. Thepurpose of this chapter is to server the next improved algorithm and do preliminaryresearch.②Huge computation and low tracking precision are the defects of the traditionalSIFT-based moving object tracking method, which may cause tracking failure. To solvethese problems, an improved method based on SURF and object region prediction ispresented. SURF is used to detect the object position in the current frame. At then,object centriod is obtained after calculated and the the object region is predicted basedon the object position information. At the same time, Kalman filter is adopted to predictthe object centriod in the next frame. Only in the prediction region, the object isobtained by SURF template matching. To further reduce tracking error rate, thehistogram re-matching method is used. The results of the experiment demonstrate thatthe improved method show better tracking effect than Mean Shift tracker, template match tracker and SIFT tracker.③In the research of multi-camera coordination object tracking method, Some keytechnology and problems have studied in this chapter. Especially, object handoveralgorithm based on object template has made the focus. In this paper, the improvedmethod adopts the many template image to match in next camera. More template image,which reflect the multiple object profile information, improve the matching success rate.The results of the experiment show that the algorithm has high accuracy and simpleoperation, which based on SURF template model for object handover.