Presentation information

General Session

General Session » GS-7 Vision, speech media processing

[4I4-GS-7e] 画像音声メディア処理:応用

Fri. Jun 11, 2021 3:40 PM - 5:20 PM Room I (GS room 4)

座長:岩澤 有祐(東京大学)

4:00 PM - 4:20 PM

[4I4-GS-7e-02] Tennis serve fall point prediction by using two stage pose estimation

〇Kohei Hiraki1, Masahiro Suzuki1, Yutaka Matsuo1 (1. The University of Tokyo)

Keywords:Deep Learning, Tennis, serve fall point prediction

The purpose of this study is to predict the fall point of tennis serve from the pose information of the player. Compared to other racquet sports, the speed of tennis serve is faster and the range of the court is wider, which makes it more difficult to return the serve. Therefore, predicting the course of the serve and returning it are considered to be important for winning and losing points. As a previous study, there is a method to predict the fall point of a serve in table tennis. However, since the player who exists in the tennis video is smaller than the table tennis player, applying this method to tennis will cause the performance of pose estimation to deteriorate, resulting in failure in predicting the fall point. We propose to improve pose estimation performance by dividing the detection into two stages: human detection and pose estimation.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.