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Sensors 2016, 16(5), 654;

Extrinsic Calibration of Camera Networks Based on Pedestrians

Image Processing and Interpretation, TELIN, Ghent University/iMinds, Sint Pietersnieuwstraat 41, 9000 Gent, Belgium
Author to whom correspondence should be addressed.
Academic Editor: Fabrizio Lamberti
Received: 24 March 2016 / Revised: 25 April 2016 / Accepted: 4 May 2016 / Published: 9 May 2016
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1635 KB, uploaded 9 May 2016]   |  


In this paper, we propose a novel extrinsic calibration method for camera networks by analyzing tracks of pedestrians. First of all, we extract the center lines of walking persons by detecting their heads and feet in the camera images. We propose an easy and accurate method to estimate the 3D positions of the head and feet w.r.t. a local camera coordinate system from these center lines. We also propose a RANSAC-based orthogonal Procrustes approach 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 that minimizes the reprojection error. While existing state-of-the-art calibration methods explore epipolar geometry and use image positions directly, the proposed method first computes 3D positions per camera and then fuses the data. This results in simpler computations and a more flexible and accurate calibration method. Another advantage of our method is that it can also handle the case of persons walking along straight lines, which cannot be handled by most of the existing state-of-the-art calibration methods since all head and feet positions are co-planar. This situation often happens in real life. View Full-Text
Keywords: extrinsic calibration; camera network; pedestrians; orthogonal Procrustes extrinsic calibration; camera network; pedestrians; orthogonal Procrustes

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Guan, J.; Deboeverie, F.; Slembrouck, M.; Van Haerenborgh, D.; Van Cauwelaert, D.; Veelaert, P.; Philips, W. Extrinsic Calibration of Camera Networks Based on Pedestrians. Sensors 2016, 16, 654.

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