Visual object tracking via non-local correlation attention learning

Siamese-based trackers have achieved remarkable advancements in performance of visual object tracking. The similarity matrix computed is crucial to Siamese-based tracker. However, the similarity matrix is lack of long-range dependency information which may lead to tracking drift on challenging scenes, like significant deformation, background clutter and occlusion. To address the above issue, this paper proposes a Siamese network with non-local correlation attention (SiamNCA). First, a non-local correlation attention module is proposed to integrate the long-range information into the similarity matrix, and give each sample in the search patch a weight based on their similarity to the template. Second, bi-directional features fusion module is introduced to fuse different similarity matrixes obtained with different level features. Finally, comprehensive experiments on representative tracking benchmarks, including OTB2015, VOT-2018, LaSOT and GOT-10k, reveal that the two modules can improve the performance of the baseline method in challenging scenes, and SiamNCA achieves state-of-art. For the average running speed, SiamNCA can achieve 43 FPS in real time. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a Siamese network with non-local correlation attention (SiamNCA) to improve the performance of Siamese-based trackers in challenging scenes. The introduced non-local correlation attention module and bi-directional features fusion module show promising results and achieve state-of-art performance.