TY - GEN
T1 - Video Annotation Tool using Human Pose Estimation for Sports Training
AU - Diogo, João
AU - Rodrigues, Rui
AU - Madeira, Rui Neves
AU - Correia, Nuno
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/OE/2020.09417.BD/PT#
supported by the project WEAVE, Grant Agreement Number: INEA/CEF/ICT/A2020/ 2288018.
supported by NOVA LINCS Research Center, partially funded by project UID/CEC/04516/2020 granted by FCT.
Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/11/27
Y1 - 2022/11/27
N2 - This paper presents and discusses the integration of human pose estimation techniques into an existing web-based multimodal video annotation tool, applying it to the sports context, where basketball is the first case study. The relevance of video analysis extends across many fields of work (e.g., professional sports, education). In sports, systematic, detailed analysis using videos of players and teams is vital to evaluate many aspects of both training and competition. MotionNotes annotation tool now combines human pose and motion information with existing traditional annotation mechanisms (e.g., text and drawings annotations), allowing users to add further details to their annotation work. The paper reports feedback from a pilot study based on a participatory workshop involving people with relevant competitive experience in basketball. Based on this use case feedback, we conclude with an outlook of future iterations for our video annotation tool.
AB - This paper presents and discusses the integration of human pose estimation techniques into an existing web-based multimodal video annotation tool, applying it to the sports context, where basketball is the first case study. The relevance of video analysis extends across many fields of work (e.g., professional sports, education). In sports, systematic, detailed analysis using videos of players and teams is vital to evaluate many aspects of both training and competition. MotionNotes annotation tool now combines human pose and motion information with existing traditional annotation mechanisms (e.g., text and drawings annotations), allowing users to add further details to their annotation work. The paper reports feedback from a pilot study based on a participatory workshop involving people with relevant competitive experience in basketball. Based on this use case feedback, we conclude with an outlook of future iterations for our video annotation tool.
KW - Human Pose and Motion
KW - People Tracking
KW - Performing Sports
KW - Video Analysis
KW - Video Annotation
UR - http://www.scopus.com/inward/record.url?scp=85145876844&partnerID=8YFLogxK
U2 - 10.1145/3568444.3570592
DO - 10.1145/3568444.3570592
M3 - Conference contribution
AN - SCOPUS:85145876844
T3 - ACM International Conference Proceeding Series
SP - 262
EP - 265
BT - Proceedings of MUM 2022, the 21st International Conference on Mobile and Ubiquitous Multimedia
A2 - Doring, Tanja
A2 - Boll, Susanne
A2 - Colley, Ashley
A2 - Esteves, Augusto
A2 - Guerreiro, João
PB - Association for Computing Machinery
CY - New York
T2 - 21st International Conference on Mobile and Ubiquitous Multimedia, MUM 2022
Y2 - 27 November 2022 through 30 November 2022
ER -