  In the last decades, visual target tracking has been one of the primary
research interests of the Robotics research community. The recent advances in
Deep Learning technologies have made the exploitation of visual tracking
approaches effective and possible in a wide variety of applications, ranging
from automotive to surveillance and human assistance. However, the majority of
the existing works focus exclusively on passive visual tracking, i.e., tracking
elements in sequences of images by assuming that no actions can be taken to
adapt the camera position to the motion of the tracked entity. On the contrary,
in this work, we address visual active tracking, in which the tracker has to
actively search for and track a specified target. Current State-of-the-Art
approaches use Deep Reinforcement Learning (DRL) techniques to address the
problem in an end-to-end manner. However, two main problems arise: i) most of
the contributions focus only on discrete action spaces and the ones that
consider continuous control do not achieve the same level of performance; and
ii) if not properly tuned, DRL models can be challenging to train, resulting in
a considerably slow learning progress and poor final performance. To address
these challenges, we propose a novel DRL-based visual active tracking system
that provides continuous action policies. To accelerate training and improve
the overall performance, we introduce additional objective functions and a
Heuristic Trajectory Generator (HTG) to facilitate learning. Through an
extensive experimentation, we show that our method can reach and surpass other
State-of-the-Art approaches performances, and demonstrate that, even if trained
exclusively in simulation, it can successfully perform visual active tracking
even in real scenarios.
