VARS: Video Assistant Referee System for Automated Soccer Decision
Making from Multiple Views
- URL: http://arxiv.org/abs/2304.04617v1
- Date: Mon, 10 Apr 2023 14:33:05 GMT
- Title: VARS: Video Assistant Referee System for Automated Soccer Decision
Making from Multiple Views
- Authors: Jan Held, Anthony Cioppa, Silvio Giancola, Abdullah Hamdi, Bernard
Ghanem, Marc Van Droogenbroeck
- Abstract summary: The Video Assistant Referee has revolutionized association football, enabling referees to review incidents on the pitch.
However, due to the lack of referees in many countries and the high cost of the VAR infrastructure, only professional leagues can benefit from it.
We propose a Video Assistant Referee System ( VARS) that can automate soccer decision-making.
- Score: 70.70161449930127
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Video Assistant Referee (VAR) has revolutionized association football,
enabling referees to review incidents on the pitch, make informed decisions,
and ensure fairness. However, due to the lack of referees in many countries and
the high cost of the VAR infrastructure, only professional leagues can benefit
from it. In this paper, we propose a Video Assistant Referee System (VARS) that
can automate soccer decision-making. VARS leverages the latest findings in
multi-view video analysis, to provide real-time feedback to the referee, and
help them make informed decisions that can impact the outcome of a game. To
validate VARS, we introduce SoccerNet-MVFoul, a novel video dataset of soccer
fouls from multiple camera views, annotated with extensive foul descriptions by
a professional soccer referee, and we benchmark our VARS to automatically
recognize the characteristics of these fouls. We believe that VARS has the
potential to revolutionize soccer refereeing and take the game to new heights
of fairness and accuracy across all levels of professional and amateur
federations.
Related papers
- SoccerNet 2024 Challenges Results [152.8534707514927]
SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
The challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding.
This year, the challenges encompass four vision-based tasks.
arXiv Detail & Related papers (2024-09-16T14:12:22Z) - Towards AI-Powered Video Assistant Referee System (VARS) for Association Football [58.04352163544319]
Video Assistant Referee ( VAR) is an innovation that enables backstage referees to review incidents on the pitch from multiple points of view.
The VAR is currently limited to professional leagues due to its expensive infrastructure and the lack of referees worldwide.
We present the semi-automated Video Assistant Referee System ( VARS) that leverages the latest findings in multi-view video analysis.
arXiv Detail & Related papers (2024-07-17T11:09:03Z) - Deep Understanding of Soccer Match Videos [20.783415560412003]
Soccer is one of the most popular sport worldwide, with live broadcasts frequently available for major matches.
Our system can detect key objects such as soccer balls, players and referees.
It also tracks the movements of players and the ball, recognizes player numbers, classifies scenes, and identifies highlights such as goal kicks.
arXiv Detail & Related papers (2024-07-11T05:54:13Z) - X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model [56.393522913188704]
We introduce the Explainable Video Assistant Referee System, X- VARS, a multi-modal large language model designed for understanding football videos from the point of view of a referee.
X- VARS can perform a multitude of tasks, including video description, question answering, action recognition, and conducting meaningful conversations.
We validate X- VARS on our novel dataset, SoccerNet-XFoul, which consists of more than 22k video-question-answer triplets annotated by over 70 experienced football referees.
arXiv Detail & Related papers (2024-04-07T12:42:02Z) - Video-based Analysis of Soccer Matches [15.328109388727997]
This paper provides a comprehensive overview and categorization of the methods developed for the video-based visual analysis of soccer matches.
We identify and discuss open research questions, soon enabling analysts to develop winning strategies more efficiently.
arXiv Detail & Related papers (2021-05-11T09:01:02Z) - SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of
Broadcast Soccer Videos [71.72665910128975]
SoccerNet-v2 is a novel large-scale corpus of manual annotations for the SoccerNet video dataset.
We release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos.
We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection.
arXiv Detail & Related papers (2020-11-26T16:10:16Z) - Game Plan: What AI can do for Football, and What Football can do for AI [83.79507996785838]
Predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision.
We illustrate that football analytics is a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI.
arXiv Detail & Related papers (2020-11-18T10:26:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.