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• W4312778522 abstract "In recent years, Siamese-based trackers have achieved excellent performance, most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for tracking object. To solve the problem that the above method neglects the correspondence of the local information between the object and the search region and cannot adapt to the object deformation well, we propose a Siamese network-based tracker with position attention network (SiamPA). First, we use Siamese backbone network to extract template and search region features. Secondly, we adopt the box-guided object feature selection strategy to avoid similarity calculations for background information. In addition, we introduce the position attention network instead of the cross-correlation layer to learn the part-level relationship between the object and the search region features. Finally, the classification-regression sub-network is used to decode the similarity respond map obtained by the position attention network and predict the position of the object. Our contribution, one is to propose a box-guided method for refining object features, and the other is to introduce a position attention network for information embedding. Experiments on three challenging benchmarks including GOT-10k, UAVI23 and OTB-100 demonstrate that our SiamPA achieves excellent tracking performance with a real-time speed." @default.
• W4312778522 created "2023-01-05" @default.
• W4312778522 creator A5023038428 @default.
• W4312778522 creator A5051647292 @default.
• W4312778522 creator A5051900665 @default.
• W4312778522 date "2022-07-25" @default.
• W4312778522 modified "2023-09-28" @default.
• W4312778522 title "Position Attention Network for Object Tracking" @default.
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• W4312778522 doi "https://doi.org/10.23919/ccc55666.2022.9901910" @default.
• W4312778522 hasPublicationYear "2022" @default.
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