Smart Video Assistant Referee (VAR)

Offside calls in sports are integral to maintaining fairness and integrity in competitive matches. With technological advancements, there is growing interest in automating the offside decision-making process to enhance accuracy and efficiency. This study leverages the YOLOv8 object detection model to assess the feasibility of integrating automation into current officiating processes. By using YOLOv8, the study aims to evaluate its potential as a reliable tool for improving the effectiveness and accuracy of offside decisions in sports. Furthermore, the research explores the impact of automated offside calls on various aspects, including game dynamics, fairness, and the spectator experience. Through a thorough examination of existing offside detection technologies such as video assistant referee (VAR) systems and computer vision algorithms, this study provides insight into how deep learning models such as YOLOv8 can aid referees in the judgment of offside decisions.