Real-Time Football Analytics: AI-Powered Object Detection, Player Metrics, and Advanced Match Insights

Abstract

This paper explores the application of AI and computer vision in real-time football analytics. By utilizing object detection, tracking, and performance metrics, this study presents a comprehensive system for analyzing football matches. Key aspects include player movement tracking, team classification, performance analysis, and real-time insights. The project integrates YOLOv8 for object detection, KMeans clustering for team classification, and various computer vision techniques to provide a robust football analysis tool.

Introduction

Football analytics is revolutionized by AI and machine learning technologies. This project focuses on providing real-time insights into football matches by leveraging AI-driven techniques. The goal is to assess player performance, analyze team strategies, and evaluate match dynamics through the use of object detection and tracking algorithms. The following methods were utilized to achieve this:

• YOLOv8 for object detection
• KMeans Clustering for team classification
• Optical Flow for camera movement analysis
• Perspective Transformation for real-world measurements

GITHUB : https://github.com/ARSHDEEPSINGHDEVELOPS/computervision-football
LINKEDIN : https://www.linkedin.com/feed/update/urn:li:activity:7228552865768689664/