Robust Visual Tracking Using Structure-Preserving Sparse Learning

Even though numerous visual tracking methods have been proposed to deal with image streams, it is a still challenging problem to facilitate a tracking method to accurately distinguish the target from the background without drifting under the severe appearance variation of target caused by distortion of local structures. For preserving local structures of target template datasets, we present a novel structure-preserving sparse learning algorithm by obtaining sparse coefficients under maximum margin projection-based subspace representation and by updating the sparse codes under multiple task feature selection framework. To reinforce local structures of targets, we adopted a novel optimization process using an accelerated proximal gradient shrinkage operation and an efficient stopping criterion. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods.