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In this paper we present a novel visual tracking algorithm, in which object tracking is achieved by using subspace learning and Huber loss regularization in a particle filter framework. The changing appearance of tracked target is modeled by principle component analysis basis vectors and row group sparsity. This method takes advantage of the strengths of subspace representation and explicitly takes...
We propose an online tracking algorithm in which the object tracking is achieved by using subspace learning and non-negative matrix factorization (NMF) under the partile filtering framework. The object appearance is modeled by a non-negative combination of non-negative components learned from examples observed in previous frames. In order to robust tracking an object, group sparsity constraints are...
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