Research on Pass Detection from Broadcast Soccer Videos

Abstract

In recent years, the use of data in sports has accelerated. For instance, in the domain of soccer, there is ongoing research aimed at leveraging tracking and event data to facilitate tactical analysis and performance evaluation. However, the process of acquiring such data frequently necessitates the laborious task of manual annotation or the utilization of costly, specialized equipment. In particular, event data is generally collected by human operators who watch match videos and annotate events using proprietary software, which involve considerable time and cost. In this research, we propose a method to automatically detect passes from broadcast soccer videos and efficiently obtain event data. In proposed method, extracts the positional information of players and the ball from the video, as well as the image features of the player closest to the ball, and uses this information to estimate whether a pass is occurring in each frame. By focusing on broadcast videos, the potential exists to undertake large-scale analyses using past and globally distributed match videos.