A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep...

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Detalles Bibliográficos
Autores: Javadiha, Mohammadreza, Andujar, Carlos, Lacasa Claver, Enrique
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/85274
Acceso en línea:https://doi.org/10.3390/s23010441
http://hdl.handle.net/10459.1/85274
Access Level:acceso abierto
Palabra clave:Sports science
Racket sports
Video-based analysis
Player tracking
Sport analytics
Data analysis
Data visualization
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spelling A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel MatchesJavadiha, MohammadrezaAndujar, CarlosLacasa Claver, EnriqueSports scienceRacket sportsVideo-based analysisPlayer trackingSport analyticsData analysisData visualizationRecent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.This research was funded by the Spanish Ministry of Science and Innovation and FEDER funds, grant number PID2021-122136OB-C21, MCIN/AEI/10.13039/501100011033/FEDER, UE.MDPI202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/s23010441http://hdl.handle.net/10459.1/85274http://hdl.handle.net/10459.1/85274reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-122136OB-C21Reproducció del document publicat a https://doi.org/10.3390/s23010441Sensors, 2023, vol. 23, núm. 1, 441cc-by (c) Mohammadreza Javadiha, Carlos Andujar, Enrique Lacasa, 2023info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/852742026-05-29T05:05:01Z
dc.title.none.fl_str_mv A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
title A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
spellingShingle A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
Javadiha, Mohammadreza
Sports science
Racket sports
Video-based analysis
Player tracking
Sport analytics
Data analysis
Data visualization
title_short A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
title_full A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
title_fullStr A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
title_full_unstemmed A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
title_sort A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
dc.creator.none.fl_str_mv Javadiha, Mohammadreza
Andujar, Carlos
Lacasa Claver, Enrique
author Javadiha, Mohammadreza
author_facet Javadiha, Mohammadreza
Andujar, Carlos
Lacasa Claver, Enrique
author_role author
author2 Andujar, Carlos
Lacasa Claver, Enrique
author2_role author
author
dc.subject.none.fl_str_mv Sports science
Racket sports
Video-based analysis
Player tracking
Sport analytics
Data analysis
Data visualization
topic Sports science
Racket sports
Video-based analysis
Player tracking
Sport analytics
Data analysis
Data visualization
description Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/s23010441
http://hdl.handle.net/10459.1/85274
http://hdl.handle.net/10459.1/85274
url https://doi.org/10.3390/s23010441
http://hdl.handle.net/10459.1/85274
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-122136OB-C21
Reproducció del document publicat a https://doi.org/10.3390/s23010441
Sensors, 2023, vol. 23, núm. 1, 441
dc.rights.none.fl_str_mv cc-by (c) Mohammadreza Javadiha, Carlos Andujar, Enrique Lacasa, 2023
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Mohammadreza Javadiha, Carlos Andujar, Enrique Lacasa, 2023
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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