UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism
Pages 366 - 371
Abstract
To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene.
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Index Terms
- UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
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- Research-article
- Research
- Refereed limited
Funding Sources
- Graduate Innovation Fund of Xi'an University of Posts and Telecommunications
- National Natural Science Foundation of China
- Key Research and Development Project of Shaanxi Province
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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