Robust tracking via discriminative sparse feature selection

In this paper, we propose a novel generative tracking approach based on discriminative sparse feature selection. The sparse features are the discriminative sparse representation of samples, which are achieved by learning a compact and discriminative dictionary. Besides the target templates, the proposed approach also incorporates the close-background templates to approximate the partial variations. We learn the dictionary and a classifier together, and search the tracking result with the maximum similarity and the minimal reconstruction error criterion using the discrimination of sparse features. In addition, we resample the close-background templates and update the dictionary in an adaptive way during tracking. Experimental results on several challenging video sequences demonstrate that the proposed approach has more favorable performance than the state-of-the-art approaches.