点击次数：

论文类型：会议论文

发表时间：2015-01-01

收录刊物：CPCI-S、SCIE

关键字：Incrementally subspace learning; low-rank modeling; visual tracking; particle filter

摘要：Subspace methods have attracted increasing attention for visual tracking. However, most previous work only aim to pursuit the subspace basis to represent appearances, thus cannot reveal the rich structure information in real world videos. This paper proposes a guided low-rank subspace learning framework to simultaneously extract the orthogonal subspace basis, the low-rank coefficients and the sparse errors to build observation model. Benefiting from the predefined guidance, we can successfully extract the relationship between the candidate particles and the subspace basis, thus most of the background can be suppressed. By reformulating the proposed model into two simple subproblems, we further develop an efficient online optimization scheme for our tracking system. Extensive experiments well validate the effectiveness and stability of our tracker over other state-of-the-art methods.