Robust visual tracking based on product sparse coding

In this paper, we propose a sparse coding tracking algorithm based on the Cartesian product of two sub-codebooks. The original sparse coding problem is decomposed into two sub sparse coding problems. And the dimension of sparse representation is intensively enlarged at a lower computational cost. Furthermore, in order to reduce the number of L1-norm minimization, ridge regression is employed to exclude the substantive outlying particles according to the reconstruction error. Finally the high-dimension sparse representation is put into the classifier and the candidate with the maximal response is considered as the target. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art algorithms. The PSC under multi-task is more robust and stable than original PSC.SVM has better generalization under PSC than under SC.The PSCT has stronger discriminative ability than SCT under the same conditions.