  Over the past decade, the technology used by referees in football has
improved substantially, enhancing the fairness and accuracy of decisions. This
progress has culminated in the implementation of the Video Assistant Referee
(VAR), an innovation that enables backstage referees to review incidents on the
pitch from multiple points of view. However, the VAR is currently limited to
professional leagues due to its expensive infrastructure and the lack of
referees worldwide. In this paper, we present the semi-automated Video
Assistant Referee System (VARS) that leverages the latest findings in
multi-view video analysis. VARS sets a new state-of-the-art on the
SoccerNet-MVFoul dataset, a multi-view video dataset of football fouls. Our
VARS achieves a new state-of-the-art on the SoccerNet-MVFoul dataset by
recognizing the type of foul in 50% of instances and the appropriate sanction
in 46% of cases. Finally, we conducted a comparative study to investigate human
performance in classifying fouls and their corresponding severity and compared
these findings to our VARS. The results of our study highlight the potential of
our VARS to reach human performance and support football refereeing across all
levels of professional and amateur federations.
