[3Xin2] Poster session 1

[3Xin2-47] Front line defensive evaluation based on event detection from full pitch video in soccer

〇Kenjiro Ide¹, Ikuma Uchida³, Rikuhei Umemoto¹, Keisuke Fujii^1,2 (1.Nagoya University, 2.Institute of Physical and Chemical Research, 3.University of Tsukuba)

Keywords:Machine Learning, Sport, Video processing

Most data analysis in soccer requires tracking data of players and ball and event data, and currently well-funded professional leagues are collecting a lot of data manually through companies. Previous research has used machine learning models to perform specific event detection from professional broadcast video, but amateur video is difficult to be analyzed due to the existence of various recording conditions. The purpose of this study is to perform specific event detection from amateur full-pitch video and to examine the validity of front-line defensive evaluation based on the detection results. In this study, two methods were used to detect events from video: the first is a deep learning-based e2e-spot method that uses video frames as input and predictive labels as output; the second is a rule-based method that uses ball velocity from ball tracking data and detects the start of a pass based on changes in velocity. We performed the fine-tuning of e2e-spot on amateur full-pitch video, was able to detect events more accurately than the rule-based method. We examined how accurately the estimated events can be used to evaluate front line defenses in soccer.