Robust visual tracking via nonlocal regularized multi-view sparse representation

作者：

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.

摘要

•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.

论文关键词：Sparse representation,Visual tracking,Multi-view learning,Dual group structure

论文评审过程：Received 20 March 2018, Revised 7 September 2018, Accepted 9 November 2018, Available online 10 November 2018, Version of Record 27 December 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.005