On-line learning tracking-by-segmentation of spacecraft observed by satellite-based imaging system

Tracking a spacecraft with the satellite-based imaging system is an essential task to on-orbit servicing, status monitoring, and visual navigation in the field of remote sensing. Tracking-by-segmentation uses the silhouette to describe the target spacecraft instead of the bounding box representation, which is robust to multiple views of different poses and more suitable for spaceborne applications. But space imagery has the characteristics of low carrier-to-noise ratio, unbalanced illumination and low contrast, which hinders reliable on-board tracking. We propose a tracking-by-segmentation algorithm GCT that adaptively models target appearance with an on-line GMM learning, and applies an external segmentation technique GrabCut to realize the silhouette tracking. The on-line learning GMM provides pixel-level multimodal appearance, which can model the target with sequential viewpoint changing. On the basis of the appearance model, we use GrabCut to construct an objective function based on graph model, which can be minimized with min-cut/maxflow algorithm to track the target’s silhouette. Iterative tracking and appearance learning make our tracker suitable for the target’s multiple views and the space’s extreme imaging condition. Based on the benchmark dataset SegTrack, contrast experiments demonstrate that the proposed algorithm GCT performs favorably on performance and processing speed against the state-of-the-art algorithms such as FLS, SPT and HFT. We also evaluate GCT on the spacecraft datasets including simulation dataset and the ESA’s Speed dataset, and the results show that the proposed technique is scalable to spacecraft of different views as well as robust to the extreme conditions of space.