Unsupervised Calibration of Camera Networks and Virtual PTZ Cameras

Pan-Tilt-Zoom (PTZ) cameras are widely used in video surveillance tasks. In particular, they can be used in combination with static cameras to provide high resolution imagery of interesting events in a scene on demand. Nevertheless, PTZ cameras only provide a single trajectory at a time. Hence, engineering algorithms for common computer vision tasks, such as automatic calibration or tracking, for camera networks including PTZ cameras is difficult. Therefore, we propose a virtual PTZ (vPTZ) camera to simplify the algorithm development for such camera networks. The vPTZ camera is built on a cylindrical panoramic view of the scene and allows to reposition its field of view arbitrarily to provide several trajectories. Further, we propose an unsupervised extrinsic self-calibration method for a network of static cameras and PTZ cameras solely based on correspondences between tracks of a walking human. Our experimental results show that we can obtain accurate estimates of the extrinsic camera parameters in both, outdoor and indoor scenarios.