Robust visual tracking via nonlocal regularized multi-view sparse representation. (April 2019) - Kang, Bin; Zhu, Wei-Ping; Liang, Dong; Chen, Mingkai
Highlights: We propose a multi-view discriminant learning based sparse representation method to explore group similarity in the multi-feature space. The proposed method makes use of unreliable observation group to achieve multi-view fusion and makes different observation groups more group discriminative. The proposed sparse representation method is incorporated into a particle filter based framework to achieve robust visual tracking. Our method can achieve a better tracking performance than state-of-the-art tracking methods do. Abstract: The multi-view sparse representation based visual tracking has attracted increasing attention because the sparse representations of different object features can complement with each other. Since the robustness of different object features is actually not the same in challenging video sequences, it may contain unreliable features (the features with low robustness) in multi-view sparse representation. In this case, how to highlight the useful information of unreliable features for proper multi-feature fusion has become a tough work. To solve this problem, we propose a multi-view discriminant sparse representation method for robust visual tracking, in which we firstly divide the multi-view observations into different groups, and then estimate the sparse representations of multi-view group projections for calculating the observation likelihood. The advantages of the proposed sparse representation method are two-folds: 1) It can properly fuse the observation groups with reliable and unreliable features by using an online updated discriminant matrix to explore the group similarity in multi-feature space. 2) It introduces a nonlocal regularizer to enforce the spatial smoothness among the sparse representations of different group projections, which can enhance the robustness of multi-view sparse representation. Experimental results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.