Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Self-Calibration of a Camera from Video of a Walking Human
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bayesian Autocalibration for Surveillance
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Localization and Trajectory Reconstruction in Surveillance Cameras with Nonoverlapping Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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 multiview 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.