A Joint Siamese Attention-Aware Network for Vehicle Object Tracking in Satellite Videos
Wei Song, Licheng Jiao, Fang Liu, Xu Liu, Lingling Li, Shuyuan Yang, Biao Hou, Wenhua Zhang
Abstract
Remote sensing object tracking is a novel and challenging problem due to the negative effects of weak features and background noise. In this paper, 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 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, demonstrate the effectiveness of the proposed methods.