Real-Time Football Match Analysis with Region-Independent Player Tracking Using Deep Learning

This project explores the application of AI in analyzing football games by tracking players across the entire video frame. Unlike traditional methods that focus on limited areas, the system here uses YOLO for detecting players everywhere in the frame and ByteTrack to follow them throughout the match. The goal is to get a clearer picture of each player's movement, particularly their speed and distance covered. Manual methods or GPS-based tools often fall short in providing quick, reliable data, especially in real-time scenarios. This study compensates for camera motion and adjusts for different viewpoints to get more accurate tracking results. As a way to test player identity consistency, the system randomly assigns popular player names to different tracking IDs. Experiments on public match videos show that the system can keep track of players even during zoom-ins, crowding, or partial visibility. Code snippets show how the model works in practice. Our results show that using full-frame AI tracking gives coaches more detailed tactical insights and helps them develop more effective strategies.