Motion-Aware Correlation Filter-Based Object Tracking in Satellite Videos

Object tracking in satellite videos poses a significant challenge for existing trackers due to the typical involvement of small objects, multiple similar disruptors, and occlusions. To improve the performance of remote sensing tracking, a novel motion-aware correlation filter (MACF) algorithm is developed in this study. The proposed approach provides the following improvements: 1) a motion estimation module based on the historical trajectory of the target is embedded into an enhanced spatial–temporal regularized correlation filter (CF)-based tracking framework, to suppress distractions caused by similar objects (SOs); and 2) a failure correction module is employed to deal with the occlusion problem, thereby further enhancing the tracking robustness. Extensive experiments are conducted on the publicly available VISO and SatSOT datasets, with the experimental results demonstrating that the proposed MACF algorithm achieves superior accuracy in comparison to state-of-the-art trackers. Particularly, the present approach has offered the first-place solution for the single object tracking task given in the ICPR 2022 challenge, on moving object detection and tracking in satellite videos (SatVideoDT).