AI-Driven Image Recognition System for Automated Offside and Foul Detection in Football Matches Using Computer Vision

artificial intelligence (AI) and computer vision in sports analytics has transformed decision-making processes, enhancing fairness and efficiency. This paper proposes a novel AI-driven image recognition system for automatically detecting offside and foul events in football matches. Unlike conventional methods, which rely heavily on manual intervention or traditional image processing techniques, our approach utilizes a hybrid deep learning model that combines advanced object tracking with motion analysis to deliver real-time, precise event detection. The system employs a robust, self-learning algorithm that leverages spatiotemporal features from match footage to track player movements and ball dynamics. By analyzing the continuous flow of video data, the model detects offside positions and identifies foul types such as tackles, handballs, and dangerous play-through a dynamic pattern recognition process. This multitiered approach overcomes traditional methods' limitations by accurately identifying critical events with minimal latency, even in complex, high-speed scenarios. In experiments conducted on diverse datasets of live match footage, the system achieved an overall accuracy of 99.85% for offside detection and 98.56% for foul identification, with precision rates of 98.32% and 97.12%, respectively. The system's recall rates of 97.45% for offside detection and 96.85% for foul recognition demonstrate its reliability in real-world applications. It's clear from these results that the proposed framework can automate and greatly enhance the accuracy of match analysis, making it a useful tool for both referees and broadcasters. The system's low computational overhead and growing ability make connecting to existing match broadcasting infrastructure easy. This establishes an immediate feedback loop for use during live games. This work marks a significant step forward in applying AI and computer vision for sports, introducing a powerful method to enhance the objectivity and precision of officiating in football.