Robust Object Tracking via Local Sparse Appearance Model.

In this paper, we propose a novel local sparse representation-based tracking framework for visual tracking. To deeply mine the appearance characteristics of different local patches, the proposed method divides all local patches of a candidate target into three categories, which are stable patches, valid patches, and invalid patches. All these patches are assigned different weights to consider the different importance of the local patches. For stable patches, we introduce a local sparse score to identify them, and discriminative local sparse coding is developed to decrease the weights of background patches among the stable patches. For valid patches and invalid patches, we adopt local linear regression to distinguish the former from the latter. Furthermore, we propose a weight shrinkage method to determine weights for different valid patches to make our patch weight computation more reasonable. Experimental results on public tracking benchmarks with challenging sequences demonstrate that the proposed method performs favorably against other state-of-the-art tracking methods.