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Automated player identification and indexing using two-stage deep learning network

American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced da...

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Autores principales: Liu, Hongshan, Adreon, Colin, Wagnon, Noah, Bamba, Abdul Latif, Li, Xueshen, Liu, Huapu, MacCall, Steven, Gan, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282031/
https://www.ncbi.nlm.nih.gov/pubmed/37339988
http://dx.doi.org/10.1038/s41598-023-36657-5
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author Liu, Hongshan
Adreon, Colin
Wagnon, Noah
Bamba, Abdul Latif
Li, Xueshen
Liu, Huapu
MacCall, Steven
Gan, Yu
author_facet Liu, Hongshan
Adreon, Colin
Wagnon, Noah
Bamba, Abdul Latif
Li, Xueshen
Liu, Huapu
MacCall, Steven
Gan, Yu
author_sort Liu, Hongshan
collection PubMed
description American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced data for identifying players, especially jersey numbers. In this work, we propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. First, we utilize an object detection network, a detection transformer, to tackle the player detection problem in a crowded context. Second, we identify players using jersey number recognition with a secondary convolutional neural network, then synchronize it with a game clock subsystem. Finally, the system outputs a complete log in a database for play indexing. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos. The proposed system shows great potential for implementation in and analysis of football broadcast video.
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spelling pubmed-102820312023-06-22 Automated player identification and indexing using two-stage deep learning network Liu, Hongshan Adreon, Colin Wagnon, Noah Bamba, Abdul Latif Li, Xueshen Liu, Huapu MacCall, Steven Gan, Yu Sci Rep Article American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced data for identifying players, especially jersey numbers. In this work, we propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. First, we utilize an object detection network, a detection transformer, to tackle the player detection problem in a crowded context. Second, we identify players using jersey number recognition with a secondary convolutional neural network, then synchronize it with a game clock subsystem. Finally, the system outputs a complete log in a database for play indexing. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos. The proposed system shows great potential for implementation in and analysis of football broadcast video. Nature Publishing Group UK 2023-06-20 /pmc/articles/PMC10282031/ /pubmed/37339988 http://dx.doi.org/10.1038/s41598-023-36657-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Hongshan
Adreon, Colin
Wagnon, Noah
Bamba, Abdul Latif
Li, Xueshen
Liu, Huapu
MacCall, Steven
Gan, Yu
Automated player identification and indexing using two-stage deep learning network
title Automated player identification and indexing using two-stage deep learning network
title_full Automated player identification and indexing using two-stage deep learning network
title_fullStr Automated player identification and indexing using two-stage deep learning network
title_full_unstemmed Automated player identification and indexing using two-stage deep learning network
title_short Automated player identification and indexing using two-stage deep learning network
title_sort automated player identification and indexing using two-stage deep learning network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282031/
https://www.ncbi.nlm.nih.gov/pubmed/37339988
http://dx.doi.org/10.1038/s41598-023-36657-5
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