Deep Reinforcement Learning-Based Car- Following Control for Active Visual Tracking From a Pan-Tilt Platform

Pan-Tilt Platform is essential for visually tracking moving targets over a wide range of regions. Due to the target’s unknown motion state and the tracking environment’s complexity, controlling the pan-tilt platform to keep the target at the center of the video frame becomes a challenging task. Therefore, a motion control technique relies on a model-based reinforcement learning algorithm is proposed. The pinhole model of the camera is used to convert the pixel coordinate to rotation angle. In addition to calculate the commanded angular speed of the pan-tilt platform using a car-following control model, the performance of the proposed models is performed by adopting a Deep Q-learning algorithm. The proposed motion control model is evaluated considering random movements of the target, and the experiment results prove the control performance in terms of tracking accuracy and robustness.