International Journal of Science, Engineering and TechnologyAuthors: Prajakta Musale, Nachiket Girnar, Palash Nikam, Pradip Pardhi, Prathmesh Parase, Naman Jain
Abstract: Football analytics has become an increasingly important tool for understanding team strategies and evaluating individual player performances. Traditional football analysis systems rely on expensive multi-camera setups and complex hardware requirements, limiting the ability of many analysts to perform detailed tactical analysis. This study presented the Football AI Analyzer (Version 2.0), an enhanced object detection system capable of analysing and producing tactical information from a single broadcast camera feed using YOLOv8. The methodology combined YOLOv8 for detecting players and the ball with ByteTrack and a Kalman filter for robust multi-object tracking across frames. Player positions were mapped onto a top-down view of the pitch using homography transformation. Voronoi tessellation was applied to measure spatial dominance, and Gaussian smoothing was used to generate individual player movement heatmaps. The system architecture was implemented using FastAPI for backend processing and WebSockets for real-time data streaming to a React-based dashboard. Results demonstrated that the system achieved a processing rate of 32 FPS and an IDF1 tracking score of 89.5% over a test dataset of 12,500 annotated frames. The Football AI Analyzer presents a realistic and cost-effective alternative to expensive proprietary tracking systems.
DOI: http://doi.org/10.5281/zenodo.21701314
