Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe\nocclusion and fast motion. In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and\nfractional-order variation regularization. Firstly, we utilize convex low-rank constraint to force the appearance similarity of the\ncandidate particles, so as to prune the irrelevant particles. Secondly, fractional-order variation is introduced to constrain the sparse\ncoefficient difference in the bounded variation space, which allows the difference between consecutive frames to exist, so as to\nadapt object fast motion. Meanwhile, fractional-order regularization can restrain severe occlusion by considering more adjacent\nframes information. Thirdly, we employ an inverse sparse representation method to model the relationship between target\ncandidates and target template, which can reduce the computation complexity for online tracking. Finally, an online updating\nscheme based on alternating iteration is proposed for tracking computation. Experiments on benchmark sequences show that our\nalgorithm outperforms several state-of-the-art methods, especially exhibiting better adaptability for fast motion and\nsevere occlusion.
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