Robust visual tracking via rank-constrained sparse learning

In this paper, we present an improved low-rank sparse learning method for particle filter based visual tracking, which we denote as rank-constrained sparse learning. Since each particle can be sparsely represented by a linear combination of the bases from an adaptive dictionary, we exploit the underlying structure between particles by constraining the rank of particle sparse representations jointly over the adaptive dictionary. Besides utilising a common structure among particles, the proposed tracker also suggests the most discriminative features for particle representations using an additional feature selection module employed in the proposed objective function. Furthermore, we present an efficient way to solve this learning problem by connecting the low-rank structure extracted from particles to a simpler learning problem in the devised discriminative subspace. The suggested way improves the overall computational complexity for the high-dimensional particle candidates. Finally, in order to achieve a more robust tracker, we augment the sparse representation of particles with adaptive weights, which indicate similarity between candidates and the dictionary templates. The proposed approach is extensively evaluated on the VOT 2013 visual tracking evaluation platform including 16 challenging sequences. Experimental results compared to state-of-the-art methods show the robustness and effectiveness of the proposed tracker.