  Visual object tracking was generally tackled by reasoning independently on
fast processing algorithms, accurate online adaptation methods, and fusion of
trackers. In this paper, we unify such goals by proposing a novel tracking
methodology that takes advantage of other visual trackers, offline and online.
A compact student model is trained via the marriage of knowledge distillation
and reinforcement learning. The first allows to transfer and compress tracking
knowledge of other trackers. The second enables the learning of evaluation
measures which are then exploited online. After learning, the student can be
ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker
with a simple and effective online adaptation mechanism, (iii) a tracker that
performs fusion of other trackers. Extensive validation shows that the proposed
algorithms compete with real-time state-of-the-art trackers.
