MeanShift tracking algorithm with adaptive block color histogram

Abstract

Traditional color histogram MeanShift (MS) algorithm only considered object's color statistical information, and didn't contain object's space information, so when the object color was close to the background color, the traditional MS algorithm easily caused object's tracking inaccurately or lost. Aimmed at this issue, a novel MeanShift tracking algorithm with adaptive block color histogram was proposed in this article, which determined block method by the size of the lastest enclosing rectangle and determined their weight coefficient by the Bhattacharyya coefficient of each block. Among them, the adaptive block color histogram contained adaptive block method and object's spatial information, and the weighted Bhattacharyya coefficient considered different block's influence on the overall similarity. Experimental results show that the new method which utilizes adaptive block method and weighted Bhattacharyya coefficient method is better than the traditional MS algorithm and the fixed block MS algorithm under occlusion and rotation.