Mean shift based object tracking supported by adaptive kalman filter

In this thesis, object tracking in video sequences isimplemented by using both mean shift algorithm and adaptive Kalman filter. Mean shift algorithm cannot give good results when the position of the tracked object is changed rapidly between subsequent two frames or the tracked object is occluded.In this study,initial position to searchthe tracked object is predicted by Kalman filter and then the mean shift algorithm begins to search the object in this position. Bhattacharyya coefficient, which is obtained from mean shift algorithm, is used to instantly update Kalman filters error covariance matrix and determine whether object is occluded or not. Experimental results demonstrate that the proposed method has been more efficient technique as compared to standard mean shift algorithm in case of occlusion and fast object tracking.