A visual SLAM based approach for calibration of distributed camera networks
This paper presents a concept which tackles the pose estimation problem (extrinsic calibration) for distributed, non overlapping multi camera networks. The basic idea is to use a visual SLAM technique in order to reconstruct the scene from a video which includes areas visible by each camera of the network. The reconstruction consists of a sparse, but highly accurate point cloud, representing a joint 3D reference coordinate system. Additionally, a set of 3D registered keyframes (images) are used for high resolution representation of the scene which also include a mapping between a set of 2D pixels to 3D points of the point cloud. The pose estimation of each surveillance camera is performed individually by assigning 2D 2D correspondences between pixels of the surveillance camera and pixels of similar keyframes that map to a 3D point. This allows to implicitly obtain a set of 2D 3D correspondences between pixels in the surveillance camera and their corresponding 3D points in a joint reference coordinate system. Thus the global camera pose can be estimated using robust methods for solving the perspective n point problem.
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)