Visual tracking algorithm based on improved Bhattacharyya coefficient and model update strategy

In the traditional mean shift tracking algorithm, the Bhattacharyya coefficient is an efficient method in image statistical feature matching;however, due to the influence of background feature, the optimal location obtained by Bhattacharyya coefficient may not be the exact target location. Thus, biased or even wrong location may be got in visual tracking. We presented an improved Bhattacharyya coefficient based on target-background confidence .The new coefficient effectively reduced the influence of background feature and emphasized the importance of target feature, which obviously improved the target matching accuracy compared to the original coefficient. In order to get an effective model update strategy, we synthetically analyzed the similarity of target model and background model, and estimated the reason of the disturbance. We used 4 challenging video sequences to test 5 tracking algorithms. The quantitative experimental analysis shows that the proposed algorithm has good real-time performance, it only takes 75.76 ms to track one frame and exceeds the other trackers in tracking precision. The experimental result shows the proposed algorithm can well restrain background disturbance, while effectively update the model and overcome the problem of model drifting, and the tracking algorithm is effective and robust.