  The trajectory and boundary of an orbiting satellite are fundamental
information for on-orbit repairing and manipulation by space robots. This task,
however, is challenging owing to the freely and rapidly motion of on-orbiting
satellites, the quickly varying background and the sudden change in
illumination conditions. Traditional tracking usually relies on a single
bounding box of the target object, however, more detailed information should be
provided by visual tracking such as binary mask. In this paper, we proposed a
SSLT (Saliency-based Semi-supervised Learning for Tracking) algorithm that
provides both the bounding box and segmentation binary mask of target
satellites at 12 frame per second without requirement of annotated data. Our
method, SSLT, improves the segmentation performance by generating a saliency
map based semi-supervised on-line learning approach within the initial bounding
box estimated by tracking. Once a customized segmentation model has been
trained, the bounding box and satellite trajectory will be refined using the
binary segmentation result. Experiment using real on-orbit rendezvous and
docking video from NASA (Nation Aeronautics and Space Administration),
simulated satellite animation sequence from ESA (European Space Agency) and
image sequences of 3D printed satellite model took in our laboratory
demonstrate the robustness, versatility and fast speed of our method compared
to state-of-the-art tracking and segmentation methods. Our dataset will be
released for academic use in future.
