Keio Research Highlights

AI technology is increasingly being used in sports to analyze and improve the performance of athletes and teams. In particular, video analysis is proving to be an effective method for evaluating the performance of players and developing team tactics.

"In rugby, to analyze formation using ball and player position, it is necessary to detect play events such as scrums and lineouts," explains Yoshimitsu Aoki, a professor at the Faculty of Science and Technology at Keio University. "Until now, these tasks have been carried out by specialist analysts. The aim of my research is to automate these tasks using 'AI image technology.' Specifically, the ball and players are automatically detected from the video, and passages of play such as passes, kicks, and scrums are recognized based on such information."

Aoki and his colleagues are developing an innovative rugby video analysis system using deep learning technology. They are able to track players using an object detection method called 'Faster RCNN.' RCNN, or Regions with Convolutional Neural Networks, are improved version of the more conventional Convolutional Neural Networks (CNN) that allow for object detection in addition to image classification. "We then input rugby images as teacher data and performed additional learning using CNN," says Aoki. "Sequences of play are identified by giving time-series neural networks with extracted features such as player and ball position information."

As shown in the video, the ball and players are accurately detected and tracked, while their positions are mapped on the field. Using this method, passages of play can be automatically estimated and tagged so that a user can easily search for the desired sequence.

"Using this system, statistical information from sports matches can be obtained rapidly. We are planning to apply this technology to other sports and action recognition issues."

Published online 24 December 2019

About the researcher