Model update mechanism for mean-shift tracking

In order to solve the model update problem in mean-shift based tracker, a novel mechanism is proposed Kalman filter is employed to update object model by filtering object kernel-histogram using previous model and current candidate. A self-tuning method is used for adaptively adjust all the parameters of the filters under the analysis of the filtering residuals. In addition, hypothesis testing servers as the criterion for determining whether to accept filtering result. Therefore, the tracker has the ability to handle occlusion so as to avoid over-update. The experimental results show that our method can not only keep up with the object appearance and scale changes but also be robust to occlusion Key wwtte: mean-shift, tracking, model update, Kalman filter, hypothesis testing.