Player Tracking and Analysis in Football Matches

Abstract

Football has evolved significantly over the past decades, not only in terms of gameplay and strategy but also through the integration of advanced technologies. With the rise of artificial intelligence, automated analysis of football matches has become a promising field for enhancing coaching strategies, performance evaluation, and real-time broadcasting insights. This work aims to develop a comprehensive system capable of extracting mean- ingful information from raw football match footage by leveraging computer vision techniques. The significance of this work lies in its holistic approach, combining multiple sub-tasks—each addressing a specific challenge within football video analysis—to build an end-to-end intelligent framework that contributes to the growing demand for automated sports understanding systems. The methodology followed in this work involves several stages of visual data processing and model design. Initially, a semantic segmentation approach was applied to separate relevant entities from the field; however, its performance proved insufficient in complex scenes. This led to the adoption of object detection methods using YOLOv8 models, which were trained to detect players, referees, and goalkeepers. Due to the small size and high motion variability of the ball, a dedicated detection model was trained separately with scaled-up input images to improve ball recognition. To understand the spatial structure of the field, a keypoint detection model was implemented to localize crucial pitch landmarks and infer field di- mensions. Subsequently, a classification model was trained to identify and categorize key match events such as goals, fouls, and substitutions. The final system integrates the outputs of these models to form a unified pipeline capable of performing comprehensive football match analysis with high accuracy and efficiency. Our overar- ching goal is to reduce human errors in football officiating by leveraging various artificial intelligence techniques.