Abstract. In this work, we propose a novel tracking algorithm with real-time performance based on the ‘Actor-Critic’ framework. This framework consists of two major components: ‘Actor’ and ‘Critic’. The ‘Actor’ model aims to infer the optimal choice in a continuous action space, which directly makes the tracker move the bounding box to the object’s location in the current frame. For offline training, the ‘Critic’ model is introduced to form a ‘Actor-Critic’ framework with reinforcement learning and outputs a Q-value to guide the learning process of both ‘Actor’ and ‘Critic’ deep networks. Then, we modify the original deep deterministic policy gradient algorithm to effectively train our ‘Actor-Critic’ model for the tracking task. For online tracking, the ‘Actor’ model provides a dynamic search strategy to locate the tracked object efficiently and the ‘Critic’ model acts as a verification module to make our tracker more robust. To the best of our knowledge, this work is the first attempt to exploit the continuous action and ‘Actor-Critic’ framework for visual tracking. Extensive experimental results on popular benchmarks demonstrate that the proposed tracker performs favorably against many state-of-the-art methods, with real-time performance