Robust Object Tracking Based on Structural Local Sparse Representation and Incremental Subspace Learning

Abstract

We develop a robust tracking method based on the structural local sparse representation and incremental subspace learning. This representation exploits both partial information and spatial information of the target. The similarity obtained by pooling across the local patches helps locate the target more accurately. In addition, we develop a template update method which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less drifting and reduces the influence of the occluded target template as well. Experiments on challenging sequences with evaluation of the state-of-the-art methods show effectiveness of the proposed algorithm.