Visual tracking via weighted sparse representation

Abstract

Recently, sparse representation has been used in visual tracking, and related trackers have emerged. However, such sparse representation is not stable and has the potential to represent a candidate with dissimilar target templates. Therefore, a new tracker based weighted sparse representation (WSRT) is proposed. Specifically, to represent a candidate, each target template is weighted according to its similarity to the candidate. The bigger the similarity is, the bigger the probability of the target template to be chosen will be. The proposed tracker chooses the similar target templates to represent each candidate and reflects the locality structure between the candidate and target templates. Experimental results show that the proposed tracker has excellent performance.