Estimating Camera Pose Using Trajectories Generated by Pan-Tilt Motion

A novel method for auto-calibration of a PTZ (pan-tilt-zoom) camera network is proposed. The key idea on which it is based is to use pan-tilt motions generated by PTZ cameras themselves as calibration patterns. Generating and observing the pan-tilt motions of each camera makes it possible to estimate whole relative camera poses in a camera network. Cameras first observe circular trajectories of a marker set on another camera performing tilt motions with various pan angles. Although finding correspondences between marker points captured by the cameras is cumbersome due to network delays, that of between circular trajectories is easy. It is thus possible to use several hundred points in a circular trajectory to estimate its normal vectors and centers with high accuracy. Imposing geometric constraints on them makes it possible to eliminate ambiguities of them and obtain a unique relative camera pose directly. A novel refinement process to minimize differences between modeled pan-tilt motions and three or more circular trajectories is then performed. Experiments using synthetic data confirmed that the refinement process improved camera pose estimation accuracy in comparison to the direct estimation. An experiment using real data showed that the proposed method works properly for real cameras.