Self-calibration of large scale camera networks

Abstract

In this paper, we present a method to calibrate large scale camera networks for multi-camera computer vision applications in sport scenes. The calibration process determines precise camera parameters, both within each camera (focal length, principal point, etc) and in between the cameras (their relative position and orientation). To this end, we first extract candidate image correspondences over adjacent cameras, without using any calibration object, solely relying on existing feature matching computer vision algorithms applied on the input video streams. We then pairwise propagate these camera feature matches over all adjacent cameras using a chained, confident-based voting mechanism and a selection relying on the general displacement across the images. Experiments show that this removes a large amount of outliers before using existing calibration toolboxes dedicated to small scale camera networks, that would otherwise fail to work properly in finding the correct camera parameters over large scale camera networks. We successfully validate our method on real soccer scenes.