HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking

Tracking moving objects from space-borne satellite videos is a new and challenging task. The main difficulty stems from the extremely small size of the target of interest. First, because the target usually occupies only a few pixels, it is hard to obtain discriminative appearance features. Second, the small object can easily suffer from occlusion and illumination variation, making the features of objects less distinguishable from features in surrounding regions. Current state-of-the-art tracking approaches mainly consider high-level deep features of a single frame with low spatial resolution, and hardly benefit from inter-frame motion information inherent in videos. Thus, they fail to accurately locate such small objects and handle challenging scenarios in satellite videos. In this article, we successfully design a lightweight parallel network with a high spatial resolution to locate the small objects in satellite videos. This architecture guarantees real-time and precise localization when applied to the Siamese Trackers. Moreover, a pixel-level refining model based on online moving object detection and adaptive fusion is proposed to enhance the tracking robustness in satellite videos. It models the video sequence in time to detect the moving targets in pixels and has ability to take full advantage of tracking and detecting. We conduct quantitative experiments on real satellite video datasets, and the results show the proposed HIGH-RESOLUTION SIAMESE NETWORK (HRSiam) achieves state-of-the-art tracking performance while running at over 30 FPS.