Graph Attention Tracking
By Dongyan Guo, Yan Shao, Ying Cui, Zhenhua Wang, Liyan Zhang, Chunhua Shen. Computer Vision and Pattern Recognition, 2020
401 citations recorded by the source corpus.
Siamese network based trackers formulate the visual tracking task as a similarity matching problem. Almost all popular Siamese trackers realize the similarity learning via convolutional feature cross-correlation between a target branch and a search branch. However, since the size of target feature region needs to be pre-fixed, these cross-correlation base methods suffer from either reserving much adverse background information or missing a great deal of foreground information. Moreover, the global matching be-tween the target and search region also largely neglects the target structure and part-level information.In this paper, to solve the above issues, we propose a simple target-aware Siamese graph attention network for general object tracking. We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature. Further, instead of using the pre-fixed region cropping for template-feature-area selection, we investigate a target-aware area selection mechanism to fit the size and aspect ratio variations of different objects. Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and LaSOT demonstrate that the proposed SiamGAT outperforms many state-of-the-art trackers and achieves leading performance. Code is available at: https://git.io/SiamGAT
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/37929e9283d214a1688bd6fa9759cd6e89b2312d
How researchers see this paper
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In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Xiaogang Wang
- Wanli Ouyang
- Junsong Yuan
- Dacheng Tao
- Radu Timofte
- Lei Zhang
Citations in the corpus
- AiATrack: Attention in Attention for Transformer Visual Tracking (2022, background): “Some efforts [62,21,19] have been made to introduce the attention structure to visual tracking.”
- ODTrack: Online Dense Temporal Token Learning for Visual Tracking (2024, background): “Traditional feature matching/- fusion methods(Chen et al. 2020; Zhang et al. 2020; Guo et al. 2021; Xie et al. 2022) focus on the appearance similar-The ity of object, without considering the property that tracking instance rely on continuous cross-frame associations.”
- Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking (2022, background): “Later, GAT [14] proposes a part-based attention network to replace the global cross-correlation, improving model performance in target size and aspect ratio variations.”