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Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation
Collecting large datasets for investigations into human locomotion is an expensive and labor-intensive process. Methods for 3D human pose estimation in the wild are becoming increasingly accurate and could soon be sufficient to assist with the collection of datasets for analysis into running kinemat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462612/ https://www.ncbi.nlm.nih.gov/pubmed/37640789 http://dx.doi.org/10.1038/s41598-023-41142-0 |
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author | Baumgartner, Tobias Paassen, Benjamin Klatt, Stefanie |
author_facet | Baumgartner, Tobias Paassen, Benjamin Klatt, Stefanie |
author_sort | Baumgartner, Tobias |
collection | PubMed |
description | Collecting large datasets for investigations into human locomotion is an expensive and labor-intensive process. Methods for 3D human pose estimation in the wild are becoming increasingly accurate and could soon be sufficient to assist with the collection of datasets for analysis into running kinematics from TV broadcast data. In the domain of biomechanical research, small differences in 3D angles play an important role. More precisely, the error margins of the data collection process need to be smaller than the expected variation between athletes. In this work, we propose a method to infer the global geometry of track and field stadium recordings using lane demarcations. By projecting estimated 3D skeletons back into the image using this global geometry, we show that current state-of-the-art 3D human pose estimation methods are not (yet) accurate enough to be used in kinematics research. |
format | Online Article Text |
id | pubmed-10462612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104626122023-08-30 Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation Baumgartner, Tobias Paassen, Benjamin Klatt, Stefanie Sci Rep Article Collecting large datasets for investigations into human locomotion is an expensive and labor-intensive process. Methods for 3D human pose estimation in the wild are becoming increasingly accurate and could soon be sufficient to assist with the collection of datasets for analysis into running kinematics from TV broadcast data. In the domain of biomechanical research, small differences in 3D angles play an important role. More precisely, the error margins of the data collection process need to be smaller than the expected variation between athletes. In this work, we propose a method to infer the global geometry of track and field stadium recordings using lane demarcations. By projecting estimated 3D skeletons back into the image using this global geometry, we show that current state-of-the-art 3D human pose estimation methods are not (yet) accurate enough to be used in kinematics research. Nature Publishing Group UK 2023-08-28 /pmc/articles/PMC10462612/ /pubmed/37640789 http://dx.doi.org/10.1038/s41598-023-41142-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Baumgartner, Tobias Paassen, Benjamin Klatt, Stefanie Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation |
title | Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation |
title_full | Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation |
title_fullStr | Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation |
title_full_unstemmed | Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation |
title_short | Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation |
title_sort | extracting spatial knowledge from track and field broadcasts for monocular 3d human pose estimation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462612/ https://www.ncbi.nlm.nih.gov/pubmed/37640789 http://dx.doi.org/10.1038/s41598-023-41142-0 |
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