An Automated Deep Learning-Based VAR for Enhancing Referee Decision-Making in Football

While football is the most popular sport the world has ever seen, the question of referee decision-making has always been a shadow that hampers the spirit and justice of the game. Video Assistant Referee (VAR) was introduced to solve officiating errors through video reviews. Nonetheless, this technology has drawn heavy criticism for causing huge delays and not completely eradicating referee bias. In this research, a Tube Convolutional Neural Network (T-CNN) model is incorporated into a new VAR system as an advanced solution. By allowing for the input of spatial and temporal features for football footage, the T-CNN model processes features which allows for rapid and accurate analysis of complex match events such as offsides, fouls, and goal-line incidents. The proposed system makes gameplay more real-time while ensuring fairness by minimizing human involvement and the consequent delays in the decision-making process. Additionally, its unbiased AI-driven decisions also eliminate the challenge of home team bias. Comparative analyses with traditional VAR methods established that the T-CNN system achieves greater accuracy rate, real-time responsiveness, and fairness, ultimately transforming football officiating.