Relation Learning Reasoning Meets Tiny Object Tracking in Satellite Videos

Tiny objects in satellite videos are usually not independent individuals; there exist rich semantic and temporal relations with each other. Thus, modeling and reasoning the variation of such intrinsic relationships can be beneficial for tiny object tracking. In this article, a relation learning reasoning method is proposed for tiny object tracking in satellite videos. The core of the proposed is the relation reasoning network that consists of a key context module, a global semantic module, and a relation reasoning module sequentially. First, the key context module exploits global key contexts which explicitly or implicitly contribute to the target object, modeling the intrinsic relations with the target. Second, to reason the contribution, the global semantic module analyses the interaction between them in the same frame. Third, the relation reasoning module deduces the target based on the variation of the semantic relations among different frames. Such a relation learning reasoning approach which takes the target as the core is aligned with the satellite tiny object tracking task, significantly improves the identification performance in dense similarity (DS) scenes and the retrieval ability after completely occluded. Furthermore, the proposed method is shown to report improved qualitative and quantitative results on Jilin-1 and SkySat satellite video datasets.