Related papers: Video-based Analysis of Soccer Matches

Video-based Analysis of Soccer Matches

URL: http://arxiv.org/abs/2105.04875v1
Date: Tue, 11 May 2021 09:01:02 GMT
Title: Video-based Analysis of Soccer Matches
Authors: Maximilian T. Fischer, Daniel A. Keim, Manuel Stein
Abstract summary: 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.
Score: 15.328109388727997
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: With the increasingly detailed investigation of game play and tactics in invasive team sports such as soccer, it becomes ever more important to present causes, actions and findings in a meaningful manner. Visualizations, especially when augmenting relevant information directly inside a video recording of a match, can significantly improve and simplify soccer match preparation and tactic planning. However, while many visualization techniques for soccer have been developed in recent years, few have been directly applied to the video-based analysis of soccer matches. This paper provides a comprehensive overview and categorization of the methods developed for the video-based visual analysis of soccer matches. While identifying the advantages and disadvantages of the individual approaches, we identify and discuss open research questions, soon enabling analysts to develop winning strategies more efficiently, do rapid failure analysis or identify weaknesses in opposing teams.

Related papers

Deep learning for action spotting in association football videos [64.10841325879996]
The SoccerNet initiative organizes yearly challenges, during which participants from all around the world compete to achieve state-of-the-art performances. This paper traces the history of action spotting in sports, from the creation of the task back in 2018, to the role it plays today in research and the sports industry.
arXiv Detail & Related papers (2024-10-02T07:56:15Z)
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)
VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views [70.70161449930127]
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.
arXiv Detail & Related papers (2023-04-10T14:33:05Z)
A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications [60.3327085463545]
We present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, diving and badminton. We develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.
arXiv Detail & Related papers (2022-06-02T13:19:36Z)
A Multi-stage deep architecture for summary generation of soccer videos [11.41978608521222]
We propose a method to generate the summary of a soccer match exploiting both the audio and the event metadata. The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries.
arXiv Detail & Related papers (2022-05-02T07:26:35Z)
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos [62.686484228479095]
We propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each. The dataset is fully annotated with bounding boxes and tracklet IDs. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved.
arXiv Detail & Related papers (2022-04-14T12:22:12Z)
A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions [3.138976077182707]
This paper presents a review of sports video analysis for various applications high-level analysis. It includes detection and classification of players, tracking player or ball in sports, predicting the trajectories of player or ball, recognizing the teams strategies, classifying various events in sports.
arXiv Detail & Related papers (2022-03-03T07:49:21Z)
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.