Part-based multi-graph ranking for visual tracking
Recently, graph ranking-based methods have been introduced to visual tracking and achieved promising results due to the local structure preserving property. However, existing graph ranking-based trackers use holistic templates to construct the graphs which makes the trackers sensitive to occlusions. In this paper, we propose a part-based multi-graph ranking algorithm for robust visual tracking. In our method, template samples are divided into local parts. Multiple graphs are constructed based on different part samples and different feature representations. Then, the multiple graphs are integrated into a regularization framework with each graph assigned a weight. Furthermore, by imposing the l2,1 norm on the weight matrix of graphs, the confident parts are selected to reduce the effects of occluded ones. An effective optimization scheme is proposed to learn the weight matrix and the rank scores jointly. Experimental results on various challenging video sequences demonstrate our proposed algorithm outperforms state-of-the-art trackers.