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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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Autores principales: Javadiha, Mohammadreza, Andujar, Carlos, Lacasa, Enrique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824851/
https://www.ncbi.nlm.nih.gov/pubmed/36617041
http://dx.doi.org/10.3390/s23010441
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author Javadiha, Mohammadreza
Andujar, Carlos
Lacasa, Enrique
author_facet Javadiha, Mohammadreza
Andujar, Carlos
Lacasa, Enrique
author_sort Javadiha, Mohammadreza
collection PubMed
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.
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spelling pubmed-98248512023-01-08 A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches Javadiha, Mohammadreza Andujar, Carlos Lacasa, Enrique Sensors (Basel) Article 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. MDPI 2022-12-31 /pmc/articles/PMC9824851/ /pubmed/36617041 http://dx.doi.org/10.3390/s23010441 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Javadiha, Mohammadreza
Andujar, Carlos
Lacasa, Enrique
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
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_short A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
title_sort query language for exploratory analysis of video-based tracking data in padel matches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824851/
https://www.ncbi.nlm.nih.gov/pubmed/36617041
http://dx.doi.org/10.3390/s23010441
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