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Learning to Score Figure Skating Sport Videos.
- Source :
-
IEEE Transactions on Circuits & Systems for Video Technology . Dec2020, Vol. 30 Issue 12, p4578-4590. 13p. - Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FIGURE skating
*SPORTS films
*TALLIES
*VIDEOS
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 30
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 147575444
- Full Text :
- https://doi.org/10.1109/TCSVT.2019.2927118