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In this paper, we address robust visual tracking as an incremental low-rank features learning problem in a particle filter framework. Our new algorithm first learns the observation model by extracting low-rank features and the corresponding subspace basis of the object from the initial several frames. Then the low-rank features and sparse errors can be incrementally updated using an ℓ 1 norm...
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