Robust visual tracking via multi-feature response maps fusion using a collaborative local-global layer visual model

This paper addresses the issue of robust visual tracking, in which an effective tracker based on multi-feature fusion under a collaborative local-global layer visual model is proposed. In the local layer, we implement a novel block tracker using structural local color histograms feature based on the foreground-background discrimination analysis approach. In the global layer we implement a complementary correlation filters-based tracker using HOG feature. Finally, the local and global trackers are linearly merged in the response maps level. We choose the different merging factors according to the reliability of each combined tracker, and when both of the combined trackers are unreliable, an online trained SVM detector is activated to re-detect the target. Experiments conducted on challenging sequences show that our final merged tracker achieves favorable tracking performance and outperforms several state-of-the-art trackers. Besides, performance of the implemented block tracker is evaluated by comparing with some relevant color histograms-based trackers.