Robust visual tracking via multi-graph ranking

Object tracking is a fundamental problem in computer vision. Although much progress has been made, object tracking is still a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. To improve the reliability and effectiveness, this paper presents an approach that explores the combination of graph-based ranking and multiple feature representations for tracking. We construct multiple graph matrices with various types of visual features, and integrate the multiple graphs into a regularization framework to learn a ranking vector. In particular, the approach has exploited temporal consistency by adding a regularization term to constrain the difference between two weight vectors at adjacent frames. An effective iterative optimization scheme is also proposed in this paper. Experimental results on a variety of challenging video sequences show that the proposed algorithm performs favorably against the state-of-the-art visual tracking methods.