Feature learning in object tracking is important because the choice of the features significantly affects system's performance. In this paper, a novel online feature learning approach based on reinforcement learning is proposed. Reinforcement learning has been extensively used as a generative model of sequential decision-making that interacts with uncertain environment. We extend this technique to feature selection for object tracking, and further add human-computer interaction to reinforcement learning to reduce the learning complexity and speed the convergence rate. Experiments of the object tracking are provided to verify the effectiveness of the proposed approach.