Smart Umpiring for LBW Detection System

In cricket, Leg Before Wicket (LBW) decisions have long been a subject of controversy and debate, often relying on the subjective judgment of on-field umpires.With the advent of advanced technologies, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools to enhance decision-making accuracy in sports.The proposed system presents AI-based LBW detection systems, exploring the integration of computer vision, ball tracking procedures, pose estimation, and predictive models to improve real-time predictions and reduce human.The combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) classifiers can achieve approximately 80-90% accuracy in classification tasks such as ball detection and LBW decision-making using side-on video footage.