Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

Hirasawa, Kaito; Maeda, Keisuke; Ogawa, Takahiro; Haseyama, Miki

doi:10.3390/s21062045


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Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

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Title:	Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder
Authors:	Hirasawa, Kaito Browse this author
	Maeda, Keisuke Browse this author
	Ogawa, Takahiro Browse this author →KAKEN DB
	Haseyama, Miki Browse this author →KAKEN DB
Keywords:	multimodal variational autoencoder
	detection of important scenes
	Twitter
	sports video
	time-lags
Issue Date:	Mar-2021
Publisher:	MDPI
Journal Title:	Sensors
Volume:	21
Issue:	6
Start Page:	2045
Publisher DOI:	10.3390/s21062045
Abstract:	A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE) is presented in this paper. Tl-MVAE estimates latent features calculated from tweet, video, and audio features extracted from tweets and videos. Then, important scenes are detected by estimating the probability of the scene being important from estimated latent features. It should be noted that there exist time-lags between tweets posted by users and videos. To consider the time-lags between tweet features and other features calculated from corresponding multiple previous events, the feature transformation based on feature correlation considering such time-lags is newly introduced to the encoder in MVAE in the proposed method. This is the biggest contribution of the Tl-MVAE. Experimental results obtained from actual baseball videos and their corresponding tweets show the effectiveness of the proposed method.
Type:	article
URI:	http://hdl.handle.net/2115/82209
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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