  In the last decade many different algorithms have been proposed to track a
generic object in videos. Their execution on recent large-scale video datasets
can produce a great amount of various tracking behaviours. New trends in
Reinforcement Learning showed that demonstrations of an expert agent can be
efficiently used to speed-up the process of policy learning. Taking inspiration
from such works and from the recent applications of Reinforcement Learning to
visual tracking, we propose two novel trackers, A3CT, which exploits
demonstrations of a state-of-the-art tracker to learn an effective tracking
policy, and A3CTD, that takes advantage of the same expert tracker to correct
its behaviour during tracking. Through an extensive experimental validation on
the GOT-10k, OTB-100, LaSOT, UAV123 and VOT benchmarks, we show that the
proposed trackers achieve state-of-the-art performance while running in
real-time.
