Robust Visual Tracking by Integrating Lucas-Kanade into Mean-Shift
Lurong Shen, Xinsheng Huang, Wanying Xu, Yongbin Zheng · 2011
The mean-shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. However, the lack of template update often leads to out of adaptation to affine transformation of the object. The Lucas-Kanade algorithm has some advantages in obtaining the affine parameters. In this paper, we introduce the inverse compositional algorithm, which is equivalent to but more efficient than Lucas-Kanade algorithm, to complement the traditional mean-shift algorithm. In this method, the average of squared error (ASE) between the initial template and the object image which is warped through the obtained affine parameters is computed to decide whether to update the current template. Experimental results show that the mean-shift tracking with Lucas-Kanade algorithm (MSLK) has high tracking accuracy and good robustness to the change of appearance of the object.