Enhancing Spatial Perception for Satellite Video Target Tracking
Meiyu Chen, Peng Wang, Xue Wu · Remote Sensing · 2025
In recent years, Transformer-based target tracking algorithms have performed well in ordinary scenarios. However, when applied to satellite video scenarios, the tracking effect of the algorithms is not satisfactory due to the small size of satellite video targets, blurred features, and complex background interference. To address this issue, this paper proposes an algorithm for Enhancing Spatial Perception for Satellite Video Target Tracking (ESPTrack). This algorithm, through the spatial collaborative attention module, integrates local and global spatial information to enhance the multi-level representation of the target’s detailed features and overall structure. Meanwhile, a Gaussian prior cross-attention module is constructed. The Gaussian distribution weighting is utilized to enhance the key context information, improving the model’s ability to perceive the target’s spatial position and reducing the impact of background interference. To verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on the satellite video datasets SatSOT and OOTB. The results show that the proposed algorithm has better performance compared with the existing target tracking algorithms, and it is verified that enhancing spatial perception in complex satellite video scenarios can effectively improve tracking performance.