Enhanced Model-Free Deep-Q Network based PTZ Camera Control Method
Dongchil Kim, Sung‐Joo Park · 2019
Recently, the algorithm based on reinforcement learning for controlling PTZ(Pan/Tilt/Zoom) of a camera has been significantly increasing. To improve the accuracy of video analysis in a video surveillance environment, it is essential to get good video sources from a camera such as an appropriate object size or an object not covered by the other object. In this paper, we propose a model-free reinforcement learning based PTZ control method to support improving the accuracy of video analysis. The proposed method fine-tunes the DQN (Deep-Q Network) for smoothly controlling PTZ camera. The simulation results show that the proposed method can automatically and seamlessly manage the PTZ camera according to moving objects.