Combining Mean-Shift and Particle Filter for Object Tracking

Mean-shift is an effective algorithm for object tracking. However, it has a poor performance when the illumination condition changes fast or the tracking target being shadowed. By contract, particle filter based object tracking has a better tracking performance, but the tracking speed is much slower compared to mean-shift. Owing to the limitations of just using a single algorithm, a novel object tracking method based on both mean-shift and particle filter is proposed in this paper. A system with feedback has been constructed by the proposed method, in which the mean-shift technique is used for initial registration, and the particle filter is called to improve the performance of tracking when the tracking result with mean-shift is unconvincing. RGB color histogram is exploited as image feature and Bhattacharyya coefficient is used for measuring the similarity between object model and candidate regions. Tracking experiments evaluated on various videos show that the proposed method is well-behaved in cases that objects have shift-variant, rotation and scaling, and achieves a satisfying tracking speed.