Object Tracking in Satellite Videos Based on Siamese Network With Multidimensional Information-Aware and Temporal Motion Compensation

Abstract

The availability of many commercial satellites has created favorable conditions for tracking typical objects in remote sensing sequences, making them widely useful in numerous applications. However, small objects, multiple similar disruptors, background clutter, and occlusion are significant challenges to this field. This study proposes the novel tracker-temporal motion compensation Siamese network (Siam-TMC) for remote sensing tracking. Our method relies on a multidimensional information-aware module and a temporal motion compensation mechanism. Notably, we propose a dual branch-based Dim-Aware module that brings together foreground and high frequency information to distinguish between critical small objects and interferers. In addition, a TMComp mechanism using temporal motion information was designed to mitigate object trajectory drift through the supervision of occlusion detection. Detailed experimental comparisons on a benchmark dataset show that our method outperforms the state-of-the-art tracking models, particularly in occlusion scenarios.