One of the major challenges of model-free visual tracking problem has been
the difficulty originating from the unpredictable and drastic changes in the
appearance of objects we target to track. Existing methods tackle this problem
by updating the appearance model on-line in order to adapt to the changes in
the appearance. Despite the success of these methods however, inaccurate and
erroneous updates of the appearance model result in a tracker drift. In this
paper, we introduce a novel real-time visual tracking algorithm based on a
template selection strategy constructed by deep reinforcement learning methods.
The tracking algorithm utilizes this strategy to choose the appropriate
template for tracking a given frame. The template selection strategy is
self-learned by utilizing a simple policy gradient method on numerous training
episodes randomly generated from a tracking benchmark dataset. Our proposed
reinforcement learning framework is generally applicable to other confidence
map based tracking algorithms. The experiment shows that our tracking algorithm
runs in real-time speed of 43 fps and the proposed policy network effectively
decides the appropriate template for successful visual tracking.

该研究提出了一种基于深度强化学习方法构建的模板选择策略的实时视觉追踪算法，该算法可有效应对模型更新导致的跟踪偏移问题，可在实时速度下以 43fps 跑动。