Motion object tracking based on the low-rank matrix representation

Abstract

Motion object tracking is one of the most important research directions in computer vision. Challenges in designing a tracking method are usually caused by occlusions, noise, or illumination changes. In this paper, a robust visual tracking algorithm is proposed in order to cope with the occlusion by introducing the motion object tracking issue as a low-rank matrix representation problem. First, being the main contribution of this paper, the observation matrix composed by image sequences is decomposed into a low-rank matrix and a sparse matrix. The motion object in the image sequence forms the low-rank matrix and the occlusion on the motion object forms the sparse matrix. Then the motion object tracking is carried out using a Bayesian state under the particle filter framework. Finally, an effective alternating algorithm is utilized to solve the proposed optimization formulation. The proposed algorithm has been examined throughout several challenging image sequences, and experiment results show that it works effectively and efficiently in different situations.