Abstract: In order to improve the robustness of the visual tracking algorithm based on sparse coding, the original sparse coding problem is decomposed into two sub sparse coding problems. And the size of the codebook is intensively increased while the computational cost is decreased. Furthermore, in order to decrease the number of the1-norm minimization, ridge regression is employed to exclude the intensive outlying particles via the reconstruction error. And the sparse representation of the particles with small reconstruction error is computed on the two subcodebooks. The high-dimension sparse representation is put into the classifier and the candidate with the biggest response is recognized as the target. The experiment results demonstrate that the robustness of the proposed algorithm is improved due to the employed Cartesian product of subcodebooks.