Camera calibration using vertical lines

Authors:
Jing Kong;Xianghua Ying;Songtao Pu;Yongbo Hou;Sheng Guan;Ganwen Wang;Hongbin Zha
Affiliations:
Key Laboratory of Machine Perception (Ministry of Eduction),School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Eduction),School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Eduction),School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Eduction),School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Eduction),School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Eduction),School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Eduction),School of EECS, Peking University, Beijing, China
Venue:
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Year:
2012

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Abstract

In this paper we present an easy method for multiple camera calibration with common field of view only from vertical lines. The locations of the vertical lines are known in advance. Compared to other calibration objects, the vertical lines have some good properties, since they can be easily built and can be visible by cameras in any direction simultaneously. Given 5 fixed vertical lines, an image containing them taken by a camera may provide 2 constraints in the intrinsic parameters of the camera, and extrinsic parameters can then be recovered. The calibration procedure consists of three main steps: Firstly, the image is rectified by a homography, which makes the projections of vertical lines parallel to u-axis in the rectified image. Secondly, for any vertical scan line in the rectified image, if we consider the scan line is taken by a virtual 1D camera, then we can calibrate the 1D camera. Finally, the intrinsic parameters of the original camera can be determined from the intrinsic parameters of the virtual 1D camera. By evaluating on both simulated and real data we demonstrate that our method is efficient and robust.