Structured Sparse Representation Appearance Model for Robust Visual Tracking
We propose a robust visual tracker based on structured sparse representation appearance model. The appearance of tracking target is modeled as a sparse linear combination of Eigen templates plus a sparse error due to occlusions. We address the structured sparse representation that preferably matches the practical visual tracking problem by taking the contiguous spatial distribution of occlusion into account. The sparsity is achieved by Block Orthogonal Matching Pursuit (BOMP) for solving structured sparse representation problem more efficiently. The model update scheme, based on incremental Singular Value Decomposition (SVD), guarantees the Eigen templates that are able to capture the variations of target appearance online. Then the approximation error is adopted to build a probabilistic observation model that integrates with a stochastic affine motion model to form a particle filter framework for visual tracking. Thanks to the block structure of sparse representation and BOMP, our proposed tracker demonstrates superiority on both efficiency and robustness improvement in comparison experiments with publicly available benchmark video sequences.
Tianxiang Bai Y. F. Li Yazhe Tang
Department of Manufacturing Engineering and Engineering Management,City University of Hong Kong,Kowloon,Hong Kong
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4399-4404
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）