Static Multi-Camera Factorization Using Rigid Motion

Camera networks have gained increased importance in recent years. Previous approaches mostly used point correspondences between different camera views to calibrate such systems. However, it is often difficult or even impossible to establish such correspondences. In this paper, we therefore present an approach to calibrate a static camera network where no correspondences between different camera views are required. Each camera tracks its own set of feature points on a commonly observed moving rigid object and these 2D feature trajectories are then fed into our algorithm. By assuming the cameras can be well approximated with an affine camera model, we show that the projection of any feature point trajectory onto any affine camera axis is restricted to a 13-dimensional subspace. This observation enables the computation of the camera calibration matrices, the coordinates of the tracked feature points, and the rigid motion of the object with a non-iterative trilinear factori...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Roland Angst, Marc Pollefeys
• Keywords: Camera Calibration, Computer Vision, ICCV 2009, Multilinear Algebra, Tensor Factorization