  Although short-term fully occlusion happens rare in visual object tracking,
most trackers will fail under these circumstances. However, humans can still
catch up the target by anticipating the trajectory of the target even the
target is invisible. Recent psychology also has shown that humans build the
mental image of the future. Inspired by that, we present a HAllucinating
Features to Track (HAFT) model that enables to forecast the visual feature
embedding of future frames. The anticipated future frames focus on the movement
of the target while hallucinating the occluded part of the target. Jointly
tracking on the hallucinated features and the real features improves the
robustness of the tracker even when the target is highly occluded. Through
extensive experimental evaluations, we achieve promising results on multiple
datasets: OTB100, VOT2018, LaSOT, TrackingNet, and UAV123.
