Scale Adaptation of Mean Shift Based on Graph Cuts Theory

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.