Learning to score the figure skating sports videos
This addresses the problem of automated scoring for figure skating, which could assist referees or enhance sports analytics, but it is incremental as it builds on existing video analysis methods.
The paper tackles the problem of automatically scoring figure skating videos by proposing a deep architecture with Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM to learn local and global sequential information, validated on a new FisV dataset of 500 videos and MIT-skate dataset, showing effectiveness in scoring.
This paper targets 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.