A Simplified Low Rank and Sparse Model for Visual Tracking

Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. Numerous tracking methods using low-rank and sparse constraints perform well in visual tracking. However, these methods cannot reasonably balance the two characteristics. Sparsity always pursues a sparse enough solution that ignores the low-rank structure and vice versa. Therefore, this paper replaces the low-rank and sparse constraints with 2,1 l norm. A simplified lowrank and sparse model for visual tracking (LRSVT), which is built upon the particle filter framework, is proposed in this paper. The proposed method first prunes particles which are different with the object and selects candidate particles for efficiency. A dictionary is then constructed to represent the candidate particles. The proposed LRSVT algorithm is evaluated against three related tracking methods on a set of seven challenging image sequences. Experimental results show that the LRSVT algorithm favorably performs against state-of-the-art tracking methods with regard to accuracy and execution time.

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