Wide-area external multi-camera calibration using vision graphs and virtual calibration object

In this paper we address external calibration of distributed multi-camera system intended for tracking and observing. We present a robust and efficient method for wide area calibration using virtual calibration object created by two LED markers. Our algorithm does not require for all the cameras to share common volume; only pairwise overlap is required. We assume the cameras are internally calibrated prior to deployment. Calibration is performed by waiving the calibration bar over the camera coverage area. The initial pose of the cameras is calculated using essential matrix decompositions. Global calibration is solved by automatically constructing weighted vision graph and finding optimal transformation paths between the cameras. In the optimization process, we introduce novel parametrization for two-point calibration using direction normal. The results are increased accuracy and robustness of the method under the presence of noise. In the paper, we present experimental results on a synthetic and real camera setup. We have performed image noise analysis on a synthetic wide-area setup of 5 cameras. Finally, we present the results obtained on a real setup with 12 cameras. The results obtained on the real camera setup show that our approach compensates for error propagation when the path transformation includes two to three nodes. No significant difference in reprojection error was found between the cameras on non-direct and direct path of the vision graph. The mean reprojection error for the real cameras was below 0.4 pixels.