From Single Cameras to the Camera Network: An Auto-Calibration Framework for Surveillance

摘要

This paper presents a stratified auto-calibration framework for typical large surveillance set-ups including non-overlapping cameras. The framework avoids the need of any calibration target and purely relies on visual information coming from walking people. Since in non-overlapping scenarios there are no point correspondences across the cameras the standard techniques cannot be employed. We show how to obtain a fully calibrated camera network starting from single camera calibration and bringing the problem to a reduced form suitable for multi-view calibration. We extend the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The proposed framework optimizes the objective function in a stratified manner thus suppressing the problem of local minima. Experiments with synthetic and real data validate the approach.