A Dataset & Methodology for Computer Vision based Offside Detection in Soccer

Abstract

Offside decisions are an integral part of every soccer game. In recent times, decision-making in soccer games, including offside decisions, has been heavily influenced by technology. However, in spite of the use of a Video Assistant Referee (VAR), offside decisions remain to be plagued with inconsistencies. The two major points of criticism for the VAR have been extensive delays in providing final decisions and inaccurate decisions arising from human errors. The visual nature of offside decision-making makes Computer Vision techniques a viable option for tackling these issues, by automating appropriate aspects of the process. However, the lack of a computational algorithm that captures all aspects of the offside rule, lack of an established methodology to computationally represent soccer match scenes in a way that can be utilized by such an algorithm, and the absence of a diverse, comprehensive dataset for testing these methods have stood in the way of research efforts for this problem. This paper precisely addresses each one of these obstacles, in an effort to facilitate further research in this area. The paper presents a computational offside decision algorithm for soccer match images. The methodology for creating a quantitative representation of soccer match images for this offside algorithm has also been presented as a pipeline of Computer Vision tasks. A novel dataset for evaluating this methodology has been presented, which contains a curated selection of soccer match scenes that represent the various challenges that can be faced by a system that aims to aid or automate the task of making offside decisions. Finally, this paper also details the performance of a specific set of Computer Vision tasks used in the presented pipeline, on the given dataset. The proposed system achieves an F1 score of 0.85 on the dataset. The drawbacks and areas of improvements for these methods have also been discussed in an attempt to focus future research on this task. The presented dataset and pipeline implementation code is available at: https://github.com/Neerajj9/Computer-Vision-based-Offside-Detection-in-Soccer