PADELVIC: Multicamera videos and motion capture data in padel matches

Javadiha, Mohammadreza|||0000-0002-4867-1132; Andújar Gran, Carlos Antonio|||0000-0002-8480-4713; Calvanese, Michele; Lacasa Claver, Enrique; Moyes Ardiaca, Jordi; Pontón Martínez, José Luis|||0000-0001-6576-4528; Susín Sánchez, Antonio|||0000-0002-0874-2784; Wang, Jiabo

PADELVIC: Multicamera videos and motion capture data in padel matches

Recent advances in computer vision and deep learning techniques have opened new possibilities regarding the automatic labeling of sport videos. However, an essen-tial requirement for supervised techniques is the availability of accurately labeled training datasets. In this paper we present PadelVic,...

ver descrição completa

Detalhes bibliográficos
Autores:	Javadiha, Mohammadreza\|\|\|0000-0002-4867-1132, Andújar Gran, Carlos Antonio\|\|\|0000-0002-8480-4713, Calvanese, Michele, Lacasa Claver, Enrique, Moyes Ardiaca, Jordi, Pontón Martínez, José Luis\|\|\|0000-0001-6576-4528, Susín Sánchez, Antonio\|\|\|0000-0002-0874-2784, Wang, Jiabo
Formato:	artículo
Fecha de publicación:	2024
País:	España
Recursos:	Universitat Politècnica de Catalunya (UPC)
Repositorio:	UPCommons. Portal del coneixement obert de la UPC
Idioma:	inglés
OAI Identifier:	oai:upcommons.upc.edu:2117/400742
Acesso em linha:	https://hdl.handle.net/2117/400742 https://dx.doi.org/10.17398/2952-2218.2.89
Access Level:	acceso abierto
Palavra-chave:	Deep learning (Machine learning) Computer vision Sports sciences Paddle tennis Pose estimation Player tracking Sport analytic Visión por computador Estimación de la pose Seguimiento de jugadores Análisis deportivo Aprenentatge profund Visió per ordinador Ciències de l'esport Pàdel Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic

id	ES_ea16b9576fac217ccc6ead4e75f326f3
oai_identifier_str	oai:upcommons.upc.edu:2117/400742
network_acronym_str	ES
network_name_str	España
repository_id_str
spelling	PADELVIC: Multicamera videos and motion capture data in padel matchesPADELVIC: Videos multicámara y datos de captura de movimiento de un partido de pádel amateurJavadiha, Mohammadreza\|\|\|0000-0002-4867-1132Andújar Gran, Carlos Antonio\|\|\|0000-0002-8480-4713Calvanese, MicheleLacasa Claver, EnriqueMoyes Ardiaca, JordiPontón Martínez, José Luis\|\|\|0000-0001-6576-4528Susín Sánchez, Antonio\|\|\|0000-0002-0874-2784Wang, JiaboDeep learning (Machine learning)Computer visionSports sciencesPaddle tennisPose estimationPlayer trackingSport analyticVisión por computadorEstimación de la poseSeguimiento de jugadoresAnálisis deportivoAprenentatge profundVisió per ordinadorCiències de l'esportPàdelÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeoÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticRecent advances in computer vision and deep learning techniques have opened new possibilities regarding the automatic labeling of sport videos. However, an essen-tial requirement for supervised techniques is the availability of accurately labeled training datasets. In this paper we present PadelVic, an annotated dataset of an ama-teur padel match which consists of multi-view video streams, estimated positional data for all four players within the court (and for one of the players, accurate motion capture data of his body pose), as well as synthetic videos specifically designed to serve as training sets for neural networks estimating positional data from videos. For the recorded data, player positions were estimated by applying a state-of-the-art pose estimation technique to one of the videos, which yields a relatively small positional error (M=16 cm, SD=13 cm). For one of the players, we used a motion capture system providing the orientation of the body parts with an accuracy of 1.5º RMS. The highest accuracy though comes from our synthetic dataset, which provides ground-truth po-sitional and pose data of virtual players animated with the motion capture data. As an example application of the synthetic dataset, we present a system for a more accurate prediction of the center-of-mass of the players projected onto the court plane, from a single-view video of the match. We also discuss how to exploit per-frame positional data of the players for tasks such as synergy analysis, collective tactical analysis, and player profile generation.This work has received funding from project PID2021-122136OB-C21 funded by MCIN/AEI and FEDER “A way to make Europe”. Mohammadreza Javadiha and Jose Luis Ponton were also funded by the Spanish Ministry of Science, Innovation and Universities, grants PRE2018-086835 and FPU21/01927.Peer Reviewed20242024-01-0120242024-02-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/400742https://dx.doi.org/10.17398/2952-2218.2.89reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4007422026-05-27T15:37:01Z
dc.title.none.fl_str_mv	PADELVIC: Multicamera videos and motion capture data in padel matches PADELVIC: Videos multicámara y datos de captura de movimiento de un partido de pádel amateur
title	PADELVIC: Multicamera videos and motion capture data in padel matches
spellingShingle	PADELVIC: Multicamera videos and motion capture data in padel matches Javadiha, Mohammadreza\|\|\|0000-0002-4867-1132 Deep learning (Machine learning) Computer vision Sports sciences Paddle tennis Pose estimation Player tracking Sport analytic Visión por computador Estimación de la pose Seguimiento de jugadores Análisis deportivo Aprenentatge profund Visió per ordinador Ciències de l'esport Pàdel Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short	PADELVIC: Multicamera videos and motion capture data in padel matches
title_full	PADELVIC: Multicamera videos and motion capture data in padel matches
title_fullStr	PADELVIC: Multicamera videos and motion capture data in padel matches
title_full_unstemmed	PADELVIC: Multicamera videos and motion capture data in padel matches
title_sort	PADELVIC: Multicamera videos and motion capture data in padel matches
dc.creator.none.fl_str_mv	Javadiha, Mohammadreza\|\|\|0000-0002-4867-1132 Andújar Gran, Carlos Antonio\|\|\|0000-0002-8480-4713 Calvanese, Michele Lacasa Claver, Enrique Moyes Ardiaca, Jordi Pontón Martínez, José Luis\|\|\|0000-0001-6576-4528 Susín Sánchez, Antonio\|\|\|0000-0002-0874-2784 Wang, Jiabo
author	Javadiha, Mohammadreza\|\|\|0000-0002-4867-1132
author_facet	Javadiha, Mohammadreza\|\|\|0000-0002-4867-1132 Andújar Gran, Carlos Antonio\|\|\|0000-0002-8480-4713 Calvanese, Michele Lacasa Claver, Enrique Moyes Ardiaca, Jordi Pontón Martínez, José Luis\|\|\|0000-0001-6576-4528 Susín Sánchez, Antonio\|\|\|0000-0002-0874-2784 Wang, Jiabo
author_role	author
author2	Andújar Gran, Carlos Antonio\|\|\|0000-0002-8480-4713 Calvanese, Michele Lacasa Claver, Enrique Moyes Ardiaca, Jordi Pontón Martínez, José Luis\|\|\|0000-0001-6576-4528 Susín Sánchez, Antonio\|\|\|0000-0002-0874-2784 Wang, Jiabo
author2_role	author author author author author author author
dc.subject.none.fl_str_mv	Deep learning (Machine learning) Computer vision Sports sciences Paddle tennis Pose estimation Player tracking Sport analytic Visión por computador Estimación de la pose Seguimiento de jugadores Análisis deportivo Aprenentatge profund Visió per ordinador Ciències de l'esport Pàdel Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic	Deep learning (Machine learning) Computer vision Sports sciences Paddle tennis Pose estimation Player tracking Sport analytic Visión por computador Estimación de la pose Seguimiento de jugadores Análisis deportivo Aprenentatge profund Visió per ordinador Ciències de l'esport Pàdel Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description	Recent advances in computer vision and deep learning techniques have opened new possibilities regarding the automatic labeling of sport videos. However, an essen-tial requirement for supervised techniques is the availability of accurately labeled training datasets. In this paper we present PadelVic, an annotated dataset of an ama-teur padel match which consists of multi-view video streams, estimated positional data for all four players within the court (and for one of the players, accurate motion capture data of his body pose), as well as synthetic videos specifically designed to serve as training sets for neural networks estimating positional data from videos. For the recorded data, player positions were estimated by applying a state-of-the-art pose estimation technique to one of the videos, which yields a relatively small positional error (M=16 cm, SD=13 cm). For one of the players, we used a motion capture system providing the orientation of the body parts with an accuracy of 1.5º RMS. The highest accuracy though comes from our synthetic dataset, which provides ground-truth po-sitional and pose data of virtual players animated with the motion capture data. As an example application of the synthetic dataset, we present a system for a more accurate prediction of the center-of-mass of the players projected onto the court plane, from a single-view video of the match. We also discuss how to exploit per-frame positional data of the players for tasks such as synergy analysis, collective tactical analysis, and player profile generation.
publishDate	2024
dc.date.none.fl_str_mv	2024 2024-01-01 2024 2024-02-01
dc.type.none.fl_str_mv	journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv	info:eu-repo/semantics/article
format	article
dc.identifier.none.fl_str_mv	https://hdl.handle.net/2117/400742 https://dx.doi.org/10.17398/2952-2218.2.89
url	https://hdl.handle.net/2117/400742 https://dx.doi.org/10.17398/2952-2218.2.89
dc.language.none.fl_str_mv	Inglés eng
language_invalid_str_mv	Inglés
language	eng
dc.rights.none.fl_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/pdf
dc.source.none.fl_str_mv	reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC)
instname_str	Universitat Politècnica de Catalunya (UPC)
reponame_str	UPCommons. Portal del coneixement obert de la UPC
collection	UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_	1869423109346426880
score	15.300724

PADELVIC: Multicamera videos and motion capture data in padel matches

Registos relacionados