From single cameras to the camera network: an auto-calibration framework for surveillance
Proceedings of the 32nd DAGM conference on Pattern recognition
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Low-complexity scalable distributed multicamera tracking of humans
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.14 |
This paper proposes a method that localizes two surveillance cameras and simultaneously reconstructs object trajectories in 3D space. The method is an extension of the Direct Reference Plane method, which formulates the localization and the reconstruction as a system of linear equations that is globally solvable by Singular Value Decomposition. The method's assumptions are static synchronized cameras, smooth trajectories, known camera internal parameters, and the rotation between the cameras in a world coordinate system. The paper describes the method in the context of self-calibrating cameras, where the internal parameters and the rotation can be jointly obtained assuming a man-made scene with orthogonal structures. Experiments with synthetic and real--image data show that the method can recover the camera centers with an error less than half a meter even in the presence of a 4 meter gap between the fields of view.