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.

翻译：过去十年间，足球裁判所使用的技术取得了显著进步，提升了判罚的公平性与准确性。这一进展最终催生了视频助理裁判（VAR）的实施，该创新技术使得后台裁判能够从多角度回放分析场上事件。然而，由于基础设施成本高昂且全球范围内裁判资源匮乏，目前VAR仅限在职业联赛中应用。本文提出一种半自动化的视频助理裁判系统（VARS），该系统充分利用了多视角视频分析领域的最新研究成果。VARS在SoccerNet-MVFoul数据集（一个包含足球犯规行为的多视角视频数据集）上实现了新的性能突破。我们的VARS系统能够识别50%案例中的犯规类型，并在46%的情况下准确判定相应处罚，从而在该数据集上达到了当前最优水平。最后，我们通过对比研究探讨了人类在犯规分类及其严重程度判定方面的表现，并将研究结果与我们的VARS系统进行对比。研究结果凸显了VARS系统达到人类判罚水平的潜力，有望为各级职业及业余足球联盟的裁判工作提供技术支持。