The classical Mean Shift can't change the scale of tracking window in real time while tracking target is changing in size. This paper adopts graph cuts theory to the problem of scale adaptation for Mean Shift tracking. According to the result of Mean Shift iteration in every frame, implementing graph cuts using skin color Gaussian mixture model(GMM) in a small area around it, and updating tracking window size through the largest skin lump among the result of graph cuts. Experimental results clearly demonstrate that the method can reflect the real scale change of tracking target, avoid the interference of other objects in background, and has good usability and robustness. Besides it enriches manipulation method of Human Computer Interaction by controlling entertainment games.

This paper addresses the problem of small scale target tracking. The divided-by-zero problem in the weight computation of mean shift algorithm and its associated tracking interrupt problem are presented. To tackle these problems, the Parzen window density estimation method is modified to interpolate the histogram of the target candidate. Then the Kullback-Leibler distance is employed as a new similarity measure between the target model and the target candidate. Its corresponding weight computation and new location expressions are derived. On the basis of these works, we propose a new small target tracking algorithm using mean shift framework. The tracking experiments for real world video sequences show that the proposed algorithm can track the target successively and accurately. It can successfully track very small targets with only 6×12 pixels.

Research on complex shape recognition showed that the shape context algorithm is sensitive to relative position variation of articulation. Aimed at this problem, a shape recognition method is proposed based on local shape filling rate of various object silhouettes. We take each landmark point as a circle center and use as its radius. Then, under a particular radius, the ratio between the covered silhouette pixels and the total pixels is defined as local shape filling rate. Thus, different radius may form different local shape filling rates. All landmark points with different radius will constitute a characteristic matrix which can effectively reflects the entire statistical property of the object shape. Experiments on a variety of shape databases show that the novel method is insensitive to articulation and less influenced by the number of landmark points, so our algorithm has strong power in describing object details.