Interest points based collaborative tracking

In this paper, we propose a robust collaborative tracking algorithm based on interest points detection and template matching in sparse representation framework. In the proposed tracker, the target dictionary and the candidate dictionary are constructed with the patches around interest points of the previous frame and the current frame, respectively. The correspondence between target points and candidate points is computed by solving an Li minimization problem. Only the mutually matched target and candidate point pairs are selected, and the displacements of them are measured to generate the candidate targets in current frame. To find the best one among all candidate targets, each of them is sparsely represented by target templates given as benchmarks in initial frame. The candidate target with the smallest projection error produces the final tracking result. The experimental results show that the proposed tracker is superior to the state-of-the-art methods remarkably with respect to the tracking accuracy.