Knowledge Is What You Need For Active Object Tracking

Active object tracking adjusts camera poses to keep single target within field-of-view, and therefore is more complex than video object tracking. In this paper, we propose KDAOT, a knowledge driven active object tracking framework in complex scenarios like occlusion. Firstly, we utilize multi-view object appearance with occlusion knowledge. Secondly, We model view angle switches using Markov chain and select effective view angle. Thirdly, we store and predict target movements and adversarial tendency to hide from camera surveillance in trajectory knowledge. Camera pose actions are determined by artificial potential fields of these hybrid knowledge. We construct three meta-verse environments for experiments, namely, Room, Forest and Yard. Results validate the overall effectiveness of our method.