Learning to Score Figure Skating Sport Videos

This paper aims at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e ., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global...

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Bibliographic Details
Published in:	IEEE transactions on circuits and systems for video technology 2020-12, Vol.30 (12), p.4578-4590
Main Authors:	Xu, Chengming, Fu, Yanwei, Zhang, Bing, Chen, Zitian, Jiang, Yu-Gang, Xue, Xiangyang
Format:	Article
Language:	eng
Subjects:	Computational modeling Convolutional codes Datasets Deep learning Figure skating Figure skating sport videos Learning multi-scale convolutional skip LSTM self-attentive LSTM Skating Sports Task analysis Three-dimensional displays Video Videos
Online Access:	Get full text
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Summary:	This paper aims at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e ., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global sequential information in each video. Furthermore, we present a large-scale figure skating sports video dataset - FisV dataset. This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds. Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our proposed model is validated on FisV and MIT-skate datasets. The experimental results show the effectiveness of our models in learning to score the figure skating videos. The codes and datasets would be downloaded from https://github.com/loadder/MS_LSTM.git .
ISSN:	1051-8215 1558-2205