Latent label mining for group activity recognition in basketball videos

Abstract Motion information has been widely exploited for group activity recognition in sports video. However, in order to model and extract the various motion information between the adjacent frames, existing algorithms only use the coarse video‐level labels as supervision cues. This may lead to th...

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Autores principales:	Lifang Wu, Zeyu Li, Ye Xiang, Meng Jian, Jialie Shen
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/631c0cbd2e244026b895fee7983080bd
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spelling	oai:doaj.org-article:631c0cbd2e244026b895fee7983080bd2021-11-29T03:38:16ZLatent label mining for group activity recognition in basketball videos1751-96671751-965910.1049/ipr2.12265https://doaj.org/article/631c0cbd2e244026b895fee7983080bd2021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12265https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract Motion information has been widely exploited for group activity recognition in sports video. However, in order to model and extract the various motion information between the adjacent frames, existing algorithms only use the coarse video‐level labels as supervision cues. This may lead to the ambiguity of extracted features and the omission of changing rules of motion patterns that are also important sports video recognition. In this paper, a latent label mining strategy for group activity recognition in basketball videos is proposed. The authors' novel strategy allows them to obtain the latent labels set for marking different frames in an unsupervised way, and build the frame‐level and video‐level representations with two separate levels of supervision signal. Firstly, the latent labels of motion patterns are digged using the unsupervised hierarchical clustering technique. The generated latent labels are then taken as the frame‐level supervision signal to train a deep CNN for the frame‐level features extraction. Lastly, the frame‐level features are fed into an LSTM network to build the spatio‐temporal representation for group activity recognition. Experimental results on the public NCAA dataset demonstrate that the proposed algorithm achieves state‐of‐the‐art performance.Lifang WuZeyu LiYe XiangMeng JianJialie ShenWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3487-3497 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photography TR1-1050 Computer software QA76.75-76.765
spellingShingle	Photography TR1-1050 Computer software QA76.75-76.765 Lifang Wu Zeyu Li Ye Xiang Meng Jian Jialie Shen Latent label mining for group activity recognition in basketball videos
description	Abstract Motion information has been widely exploited for group activity recognition in sports video. However, in order to model and extract the various motion information between the adjacent frames, existing algorithms only use the coarse video‐level labels as supervision cues. This may lead to the ambiguity of extracted features and the omission of changing rules of motion patterns that are also important sports video recognition. In this paper, a latent label mining strategy for group activity recognition in basketball videos is proposed. The authors' novel strategy allows them to obtain the latent labels set for marking different frames in an unsupervised way, and build the frame‐level and video‐level representations with two separate levels of supervision signal. Firstly, the latent labels of motion patterns are digged using the unsupervised hierarchical clustering technique. The generated latent labels are then taken as the frame‐level supervision signal to train a deep CNN for the frame‐level features extraction. Lastly, the frame‐level features are fed into an LSTM network to build the spatio‐temporal representation for group activity recognition. Experimental results on the public NCAA dataset demonstrate that the proposed algorithm achieves state‐of‐the‐art performance.
format	article
author	Lifang Wu Zeyu Li Ye Xiang Meng Jian Jialie Shen
author_facet	Lifang Wu Zeyu Li Ye Xiang Meng Jian Jialie Shen
author_sort	Lifang Wu
title	Latent label mining for group activity recognition in basketball videos
title_short	Latent label mining for group activity recognition in basketball videos
title_full	Latent label mining for group activity recognition in basketball videos
title_fullStr	Latent label mining for group activity recognition in basketball videos
title_full_unstemmed	Latent label mining for group activity recognition in basketball videos
title_sort	latent label mining for group activity recognition in basketball videos
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/631c0cbd2e244026b895fee7983080bd
work_keys_str_mv	AT lifangwu latentlabelminingforgroupactivityrecognitioninbasketballvideos AT zeyuli latentlabelminingforgroupactivityrecognitioninbasketballvideos AT yexiang latentlabelminingforgroupactivityrecognitioninbasketballvideos AT mengjian latentlabelminingforgroupactivityrecognitioninbasketballvideos AT jialieshen latentlabelminingforgroupactivityrecognitioninbasketballvideos
_version_	1718407648716521472

Latent label mining for group activity recognition in basketball videos

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