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• W4366684940 endingPage "893" @default.
• W4366684940 startingPage "893" @default.
• W4366684940 abstract "For the Siamese network-based trackers utilizing modern deep feature extraction networks without taking full advantage of the different levels of features, tracking drift is prone to occur in aerial scenarios, such as target occlusion, scale variation, and low-resolution target tracking. Additionally, the accuracy is low in challenging scenarios of visual tracking, which is due to the imperfect utilization of features. To improve the performance of the existing Siamese tracker in the above-mentioned challenging scenes, we propose a Siamese tracker based on Transformer multi-level feature enhancement with a hierarchical attention strategy. The saliency of the extracted features is enhanced by the process of Transformer Multi-level Enhancement; the application of the hierarchical attention strategy makes the tracker adaptively notice the target region information and improve the tracking performance in challenging aerial scenarios. Meanwhile, we conducted extensive experiments and qualitative or quantitative discussions on UVA123, UAV20L, and OTB100 datasets. Finally, the experimental results show that our SiamHAS performs favorably against several state-of-the-art trackers in these challenging scenarios." @default.
• W4366684940 created "2023-04-24" @default.
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• W4366684940 date "2023-04-21" @default.
• W4366684940 modified "2023-09-30" @default.
• W4366684940 title "SiamHAS: Siamese Tracker with Hierarchical Attention Strategy for Aerial Tracking" @default.
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• W4366684940 doi "https://doi.org/10.3390/mi14040893" @default.
• W4366684940 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37421126" @default.
• W4366684940 hasPublicationYear "2023" @default.
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