Auto-calibration of Non-overlapping Multi-camera CCTV Systems

Deployment of existing vision approaches in camera networks for applications such as human tracking show a large gap between user expectation and current results. Calibrated cameras could push these approaches closer to applicability, as physical constraints greatly complement the ill-posed acquisition process. Calibrated cameras promise also new applications as spatial relationships among cameras and the environment capture additional information. However, a convenient calibration is still a challenge on its own. This paper presents a novel calibration framework for large networks including non-overlapping cameras. The framework purely relies on visual information coming from walking people. Since non-overlapping scenarios make point correspondences impossible, time constancy of a person’s motion introduces the missing complementary information. The framework obtains calibrated cameras starting from single camera calibration thereby bringing the problem to a reduced form suitable for multi-view calibration. It extends the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The stratified optimization suppresses the danger to get stuck in local minima. Experiments with synthetic and real data validate the approach.