PassID: A Modular System for Pass Detection with Integrated Player Identification in Football

Abstract

The analysis of football passes plays a crucial role in understanding team tactics and improving performance. However, current methods for capturing and analyzing this data are often inaccessible due to high costs and reliance on proprietary datasets. This thesis presents the development of an automated system designed to detect passes in football matches using video as the source of information. The system integrates computer vision and machine learning techniques across mul tiple modules, including player and ball detection, object tracking, team identification, and pass detection. Using a hybrid approach with YOLOv9 for player detection, FasterRCNN for the ball, and Norfair for tracking, the system assigns unique identifiers to players and determines passes based on proximity and ball possession changes. Team identification is achieved through color histogram analysis, allowing the system to distinguish valid passes between players of the same team. The modular design enables independent improvements in each component, providing a flexible framework that can be adapted to different match conditions. This work represents a step forward in automating football pass detection, contributing to the growing field of sports analysis with a scalable and efficient solution.