[4I4-GS-7e] 画像音声メディア処理：応用

[4I4-GS-7e-01] Element Recognition of Step Sequences in Figure Skating Using Deep Learning

〇Akiho Iwata¹, Hirono Kawashima¹, Makoto Kawano², Jin Nakazawa¹ (1. Keio University, 2. The University of Tokyo)

Keywords:Figure skate, Video analysis

In this study, we work on the automatic classification of the elements of the step sequence from the video of figure skating. In figure skating, the scoring of all performances consists of judging each element and evaluating the performance, and all are done visually by the referees. However, it is a cost for the referees to judge and evaluate the element at the same time. Therefore, by automatically recognizing the elements, the cost on the referees can be reduced, and the referees can focus on evaluating the performance.Given formulating the element recognition as a video classification problem, we need to build a figure skating dataset with several undesirable properties.
We use a convolutional neural network for element recognition with several techniques that treat the properties.
In the experiment, we conducted ablation studies to verify which technique is useful for the figure skate dataset and report the result of the studies.