Paper Info
Title
A Joint Siamese Attention-Aware Network for Vehicle Object Tracking in Satellite 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.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3184755
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Remote sensing, Object tracking, Videos, Correlation, Satellites, Feature extraction, Convergence, Attention mechanism, satellite videos, Siamese tracker, vehicle object tracking
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Wei Song125644.41
Licheng Jiao25698475.84
Fang Liu31188125.46
X.L. Liu41111.83
Lingling Li500.34
Shuyuan Yang650948.76
Biao Hou736849.04
Wenhua Zhang800.34