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Robust Siamese Tracking with Asymmetrical Feature Processing Network
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  • Zhongjie Mao,
  • Xi Chen,
  • Jia Yan,
  • Shenghua Fan
Zhongjie Mao
Wuhan University School of Computer Science
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Xi Chen
Wuhan University School of Computer Science

Corresponding Author:[email protected]

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Jia Yan
Wuhan University Department of Electrical Engineering
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Shenghua Fan
Wuhan University School of Computer Science
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The Siamese tracker consists of two components: a classification and a regression networks. Despite their different roles, most Siamese trackers have similar feature fusion modules in the two networks, leading to the neglect of their unique characteristics. In this work, we experimentally discover that the two networks place different levels of emphasis on different types of information. Specifically, regression tends to rely on semantic information, while classification places more emphasis on global information. Therefore, we propose a new tracking structure named SGTrack, which includes a semantic augmentation fusion (SAF) for regression and a global relevance fusion (GRF) for classification. It allows us to unlock the full potential of both networks. The experimental results of our method on five benchmarks provide evidence of a notable improvement in tracking performance, while preserving real-time speed.
03 Mar 2023Submitted to Expert Systems
06 Mar 2023Assigned to Editor
06 Mar 2023Submission Checks Completed
15 Apr 2023Reviewer(s) Assigned