Copyright © 2013 Yang Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Based on sparse representation, a robust global and local classification algorithm for visual target tracking in uncertain environment was proposed in this paper. The global region of target and the position of target would be found, respectively by the proposed algorithm. Besides, overcompleted dictionary was obtained and updated by biased discriminate analysis with the divergence of positive and negative samples at current frame. And this over-completed dictionary not only discriminates the positive samples accurately but also rejects the negative samples effectively. Experiments on challenging sequences with evaluation of the state-of-the-art methods show that the proposed algorithm has better robustness to illumination changes, perspective changes, and targets rotation itself.