Football Match Analysis Using YOLOv5 for Player Detection and Tracking

In the modern era of data-driven sports analytics, football clubs and media broadcasters are increasingly adopting computer vision and artificial intelligence to extract actionable insights from match footage. This research presents a deep learning-based framework that utilizes the YOLOv5 object detection model for the automatic detection and counting of football players from video footage By leveraging pre-trained models and PyTorch frameworks, our system can process full match videos, annotate players in each frame with bounding boxes, and deliver count statistics in near real-time The proposed solution is lightweight, scalable, and suitable for both offline analysis and potential live-stream integration The system maintains robustness across various lighting and camera conditions and provides a baseline for further research in sports analytics automation