Object Tracking Algorithm Based on Combination of Dynamic Template Matching and Kalman Filter

Abstract

The moving object detection is a prerequisite and difficult point to realize tracking in the video tracking system. In order to detect moving object effectively, an object tracking algorithm is proposed based on combination of dynamic template matching and Kalman filter. First, get the area of the moving object by using inter-frame difference method and extract the SIFT feature points. Then, find the location of the candidate object that is most matched with the object template in the search area by Kalman filter and match it with the object template in the current frame. Finally, the feature points' loss rate will serve as the limited threshold, and we update template according to dynamic template updating strategy. By the number of the frames of the target's matching failures we determine whether the moving target is disappeared. Several experiments of the object tracking show that the approach is accurate and fast, and it has a better robust performance during the attitude changing, the size changing and the shelter instance.