1. A Joint Siamese Attention-Aware Network for Vehicle Object Tracking in Satellite Videos.
- Author
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Song, Wei, Jiao, Licheng, Liu, Fang, Liu, Xu, Li, Lingling, Yang, Shuyuan, Hou, Biao, and Zhang, Wenhua
- Subjects
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ARTIFICIAL neural networks , *DEEP learning , *REMOTE sensing , *ARTIFICIAL satellite tracking , *VIDEOS - Abstract
Remote sensing object tracking (RSOT) is a novel and challenging problem due to the negative effects of weak features and background noise. In this article, from the perspective of attention-focus deep learning, we propose a joint Siamese attention-aware network (JSANet) for efficient remote sensing tracking which contains both the self-attention and cross-attention modules. First, the self-attention modules we propose emphasize the interdependent channel-wise coefficient via channel attention and conduct corresponding space transformation of spatial domain information with spatial attention. Second, the cross-attention is designed to aggregate rich contextual interdependencies between the Siamese branches via channel attention and excavate association produces reliable correspondence with spatial attention. In addition, a composite feature combine strategy is designed to fuse multiple attention features. Experimental results on the Jilin-1 satellite video datasets demonstrate that the proposed JSANet achieves state-of-the-art performance in terms of precision and success rate, demonstrating the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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