STG-Siam: siamese object tracking based on sparse self attention and graph information interaction

Yining Yang; Jinsong Leng; Hua Cai; Wancheng Liu; Yuhai Li

doi:10.1117/12.2688617

6 November 2023 STG-Siam: siamese object tracking based on sparse self attention and graph information interaction

Yining Yang, Jinsong Leng, Hua Cai, Wancheng Liu, Yuhai Li

Proceedings Volume 12921, Third International Computing Imaging Conference (CITA 2023); 129211P (2023) https://doi.org/10.1117/12.2688617
Event: Third International Computing Imaging Conference (CITA 2023), 2023, Sydney, Australia

Abstract

Aiming at the problems of insufficient foreground and background discrimination ability of the tracking algorithm in complex scenes, and limited ability to cope with the situation of partial occlusion of the target, we propose a target tracking algorithm based on sparse self-attention interaction with graph information (STG-Siam). To deal with background interference during tracking, a sparse self-attention mechanism is designed to make the model focus more on foreground information and suppress attention to background information. To better handle partial occlusions during tracking, template features and search region features are mapped to graph structures, and local information interaction between graph nodes is used to generate higher quality feature response maps. Experiments on the publicly available datasets VOT2018 and UAV123 demonstrate the effectiveness of the proposed STG-Siam, which can achieve accurate and robust tracking in complex scenarios.

Citation Download Citation

Yining Yang, Jinsong Leng, Hua Cai, Wancheng Liu, and Yuhai Li "STG-Siam: siamese object tracking based on sparse self attention and graph information interaction", Proc. SPIE 12921, Third International Computing Imaging Conference (CITA 2023), 129211P (6 November 2023); https://doi.org/10.1117/12.2688617

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KEYWORDS

Detection and tracking algorithms

Ablation

Background noise

Matrices

Education and training

Human computer interaction

Image enhancement

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