Visual Tracking Using Sparsity Induced Similarity

Currently sparse signal reconstruction gains considerable interests and is applied in many fields. In this paper, we propose a new approach which utilizes the sparsity induced similarity to construct the tracking algorithm. Compared with state-of-the-art, the advantage of this approach is that the sparse representation needs to be calculated for only once and therefore the time cost is dramatically decreased. In addition, extensive experimental comparisons show that the proposed approach is more robust than some existing approaches.