Multi-camera Extrinsic Auto-calibration Using Pedestrians in Occluded Environments

• Published in: Pattern Recognition and Computer Vision Vol. 13020; pp. 204 - 215
• Main Authors: Guan, Junzhi, Geng, Hujun, Gao, Feng, Li, Chenyang, Zhang, Zeyong
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Camera network; Extrinsic calibration; Normal; Orthogonal procrustes; Pedestrians
• ISBN: 9783030880064; 3030880060
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-88007-1_17

In this paper, we propose a novel extrinsic calibration method for camera networks based on a pedestrian who walks on a horizontal surface. Unlike existing methods which require both the feet and head of the person to be visible in all views, our method only assumes that the upper body of the person is visible, which is more realistic in occluded environments. Firstly, we propose a method to calculate the normal of the plane containing all head positions of a single pedestrian. We then propose an easy and accurate method to estimate the 3D positions of a head w.r.t. to each local camera coordinate system. We apply orthogonal procrustes analysis on the 3D head positions to compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion. Finally, we refine the extrinsic calibration matrices using a method which minimizes the reprojection error. Experimental results show that the proposed method provides an accurate estimation of the extrinsic parameters.