Object Tracking Algrithm Based On Mean Shif

Object tracking is a very important work within the field of computer vision. Object tracking attracted the attention of many researchers. At present the object tracking is widely used to intelligent video surveillance, man-machine interaction, automatic driving, military security and so on. The classical Mean shift kernel-based tracking algorithm has the advantages of real-time and accuracy. And these techniques have promising future either in scientific research or applications.Mean shift algorithm is one of the classic method for target tracking. The classical mean shift tracking algorithm has the advantages of real-time and accuracy, but the target easy lost when in the complex background and track environmental changes. Some methods of objects tracking are proposed in this dissertation based on the digital image processing, statistics, and dynamic system analysis. The research work may be summarized as follows.For complex backgrounds and under varying illumination conditions mean shift algorithm for target tracking with classic apt to occur when the target is missing. We made background modeling method of effective target in the background, and made a new template update mechanism, effectively prevent does not match the template so as to improve the accuracy of target tracking.We used the block method and select the optimal match to determine the target's movement of the block position for object in occlusion. A new tracking method for object in occlusion or fast movement not is proposed to improve the Mean Shift tracking algorithm with the Kalman filter. When the tracked object is in occlusion, the Kalman filter predicts the possible position of the object. The experiment results show the improved tracking method's quickness and robustness.