Tracking Like Human: Dynamic Scene Learning Reasoning Tracker in Satellite Videos

In satellite video object tracking, the individual frame analysis method is usually used for target localization, ignoring informative cues of the dynamic scene. Temporal information could contribute to identifying the target from distractors. In this work, a novel dynamic scene learning reasoning tracker is proposed for satellite videos, which reasons over temporal dynamic information to derive the target location. It is inspired by the tracking pattern through human perception and reasoning. First, static-dynamic united analysis is designed to construct dynamic scenes by concatenating the static searching results along the temporal dimension. Second, the information of each response object is aggregated by wavelet transforms. Meanwhile, these scenes are projected into low-frequency and high-frequency subspaces, which could imitate different levels of perceptions of humans for scenes. Third, an object-aware reasoning transformer is proposed to utilize the temporal dynamics of input response objects. In each subspace, it models the mutual interactions between dynamic objects and further learns the intrinsic property of each object for target reasoning. Finally, to obtain the current reasoning result, inverse wavelet transforms are utilized to integrate the results of low-frequency and high-frequency subspaces. The effectiveness of the proposed method is validated on three public satellite video datasets, including SV248S, SkySat, and VISO. Qualitative and quantitative experimental results show that the proposed tracker outperforms 22 popular approaches in seven challenging tracking satellite scenarios.