Mean-Shift Object Tracking of Adaptive Tracking Window
自适应窗口选取的Mean-Shift目标跟踪

To overcome the problem that traditional Mean-Shift based tracking method is easy to fail because of the rigid tracking window with the changing of object scales, a new algorithm to estimate the scale of the tracking window is proposed. It divides the template image and the current frame image into several concentric cirques. After calculating the comparability measurement of corresponding cirques between the template and the current image, it ascertains the bandwidth parameter of current frame. Finally, Kalman filter is adopt to finish the update of the template scale and the stable object tracking is realized. Experiments prove that the proposed algorithm is able to renew the template accurately when the scale of the object changes, and the tracking reliability is improved compared to the traditional Mean-Shift based tracking method.