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Drone-Based Position Detection in Sports—Validation and Applications
Radio and video-based electronic performance and tracking systems (EPTS) for position detection are widely used in a variety of sports. In this paper, the authors introduce an innovative approach to video-based tracking that uses a single camera attached to a drone to capture an area of interest fro...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040709/ https://www.ncbi.nlm.nih.gov/pubmed/35492583 http://dx.doi.org/10.3389/fphys.2022.850512 |
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author | Russomanno, Tiago Guedes Blauberger, Patrick Kolbinger, Otto Lam, Hilary Schmid, Marc Lames, Martin |
author_facet | Russomanno, Tiago Guedes Blauberger, Patrick Kolbinger, Otto Lam, Hilary Schmid, Marc Lames, Martin |
author_sort | Russomanno, Tiago Guedes |
collection | PubMed |
description | Radio and video-based electronic performance and tracking systems (EPTS) for position detection are widely used in a variety of sports. In this paper, the authors introduce an innovative approach to video-based tracking that uses a single camera attached to a drone to capture an area of interest from a bird’s eye view. This pilot validation study showcases several applications of this novel approach for the analysis of game and racket sports. To this end, the authors compared positional data retrieved from video footage recorded using a drone with positional data obtained from established radio-based systems in three different setups: a tennis match during training with the drone hovering at a height of 27 m, a small-sided soccer game with the drone at a height of 50 m, and an Ultimate Frisbee match with the drone at a height of 85 m. For each type of playing surface, clay (tennis) and grass (soccer and Ultimate), the drone-based system demonstrated acceptable static accuracy with root mean square errors of 0.02 m (clay) and 0.15 m (grass). The total distance measured using the drone-based system showed an absolute difference of 2.78% in Ultimate and 2.36% in soccer, when compared to an established GPS system and an absolute difference of 2.68% in tennis, when compared to a state-of-the-art LPS. The overall ICC value for consistency was 0.998. Further applications of a drone-based EPTS and the collected positional data in the context of performance analysis are discussed. Based on the findings of this pilot validation study, we conclude that drone-based position detection could serve as a promising alternative to existing EPTS but would benefit from further comparisons in dynamic settings and across different sports. |
format | Online Article Text |
id | pubmed-9040709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90407092022-04-27 Drone-Based Position Detection in Sports—Validation and Applications Russomanno, Tiago Guedes Blauberger, Patrick Kolbinger, Otto Lam, Hilary Schmid, Marc Lames, Martin Front Physiol Physiology Radio and video-based electronic performance and tracking systems (EPTS) for position detection are widely used in a variety of sports. In this paper, the authors introduce an innovative approach to video-based tracking that uses a single camera attached to a drone to capture an area of interest from a bird’s eye view. This pilot validation study showcases several applications of this novel approach for the analysis of game and racket sports. To this end, the authors compared positional data retrieved from video footage recorded using a drone with positional data obtained from established radio-based systems in three different setups: a tennis match during training with the drone hovering at a height of 27 m, a small-sided soccer game with the drone at a height of 50 m, and an Ultimate Frisbee match with the drone at a height of 85 m. For each type of playing surface, clay (tennis) and grass (soccer and Ultimate), the drone-based system demonstrated acceptable static accuracy with root mean square errors of 0.02 m (clay) and 0.15 m (grass). The total distance measured using the drone-based system showed an absolute difference of 2.78% in Ultimate and 2.36% in soccer, when compared to an established GPS system and an absolute difference of 2.68% in tennis, when compared to a state-of-the-art LPS. The overall ICC value for consistency was 0.998. Further applications of a drone-based EPTS and the collected positional data in the context of performance analysis are discussed. Based on the findings of this pilot validation study, we conclude that drone-based position detection could serve as a promising alternative to existing EPTS but would benefit from further comparisons in dynamic settings and across different sports. Frontiers Media S.A. 2022-03-17 /pmc/articles/PMC9040709/ /pubmed/35492583 http://dx.doi.org/10.3389/fphys.2022.850512 Text en Copyright © 2022 Russomanno, Blauberger, Kolbinger, Lam, Schmid and Lames. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Russomanno, Tiago Guedes Blauberger, Patrick Kolbinger, Otto Lam, Hilary Schmid, Marc Lames, Martin Drone-Based Position Detection in Sports—Validation and Applications |
title | Drone-Based Position Detection in Sports—Validation and Applications |
title_full | Drone-Based Position Detection in Sports—Validation and Applications |
title_fullStr | Drone-Based Position Detection in Sports—Validation and Applications |
title_full_unstemmed | Drone-Based Position Detection in Sports—Validation and Applications |
title_short | Drone-Based Position Detection in Sports—Validation and Applications |
title_sort | drone-based position detection in sports—validation and applications |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040709/ https://www.ncbi.nlm.nih.gov/pubmed/35492583 http://dx.doi.org/10.3389/fphys.2022.850512 |
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