Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review


Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.
Original languageEnglish
Title of host publication2020 25th International Conference on Pattern Recognition (ICPR)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)978-1-7281-8808-9
Publication statusPublished - 2021 May 5
Event2020 25th International Conference on Pattern Recognition - Virtual, Milan, Italy
Duration: 2021 Jan 102021 Jan 15
Conference number: 25

Publication series

NameInternational Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference2020 25th International Conference on Pattern Recognition
Abbreviated titleICPR 2020
Internet address

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)


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