Robust Visual Tracking via Incremental Subspace Learning and Local Sparse Representation

Abstract

Single target tracking is an important part of computer vision, and its robustness is always restricted by target occlusion, illumination change, target pose change and so far. To deal with this problem, this paper proposed a robust visual tracking based on incremental subspace learning and local sparse representation. The algorithm adopts local sparse representation to test occlusion and rectifies the incremental learning error according to the occlusion detection outcome and to overcome the influence of occlusion on target template. Moreover, similarity between target templates and candidate templates is computed on the basis of local sparse representation. In the frame of particle filter, target tracking is achieved by combining incremental error and similarity measurement. The experimental resulting in several challenging sequences shows that the proposed method has better performance than that of state-of-the-art tracker.