Graph Attention Tracking

• URL: http://arxiv.org/abs/2011.11204v1
• Date: Mon, 23 Nov 2020 04:26:45 GMT
• Title: Graph Attention Tracking
• Authors: Dongyan Guo, Yanyan Shao, Ying Cui, Zhenhua Wang, Liyan Zhang, Chunhua Shen
• Abstract summary: We propose a simple target-aware Siamese graph attention network for general object tracking. Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and LaSOT demonstrate that the proposed SiamGAT outperforms many state-of-the-art trackers.
• Score: 76.19829750144564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 between 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

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