[3N1-IS-2d] Machine learning (4/5)

[3N1-IS-2d-05] A Tennis Racket Tip Tracking Method from Sequential Images Using A CNN

〇Taichi Hosoi¹, Hirohisa Hioki¹ (1. Kyoto University)

Keywords:Deep Learning, Image Processing, Convolutional Neural Network, Motion Analytics, Sport Analytics

Recent achievements in image processing technologies enable us to automatically extract various information from sports videos and utilize it for purposes like analyzing games. For analyzing sports played with equipment like tennis, tracking their movements matters as well as those of players. For tracking players' movements, we already have methods that can estimate joint positions from videos. Meanwhile, for equipment, although we can locate it in videos by object detection methods, such location information is not always enough for our purpose. We require more detailed information like to which direction a racket is facing. We hence propose a method to track the tip of a tennis racket in a video for analyzing its movements. Considering applicability and usability, we are aiming at making our method work for single video streams taken under various conditions (courts, racket colors, clothes and weather) and can track a racket tip stably even when it happens to be occluded by a player or looks blurred in videos. For this purpose, we employ a CNN (Convolutional Neural Network) which processes time sequential images. We have performed an experiment and found that our method seems to work better than a method processing images one by one separately.