Robust visual tracking via nonlocal regularized multi-view sparse representation

PATTERN RECOGNITION

卷:88

Article

Kang, Bin¹  Zhu, Wei-Ping²  Liang, Dong³  Chen, Mingkai⁴ 

[1] Nanjing Univ Posts & Telecommun, Coll Internet Things, Nanjing 210003, Jiangsu, Peoples R China

[2] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1M8, Canada

[3] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Jiangsu, Peoples R China

[4] Nanjing Univ Posts & Telecommun, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China

关键词: Sparse representation;    Visual tracking;    Multi-view learning;    Dual group structure;   

DOI  :  10.1016/j.patcog.2018.11.005

来源: Elsevier

【 摘 要 】

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. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free