Challenges and Opportunities for Computer Vision in Real-life Soccer
Analytics
- URL: http://arxiv.org/abs/2004.06180v1
- Date: Mon, 13 Apr 2020 20:06:23 GMT
- Title: Challenges and Opportunities for Computer Vision in Real-life Soccer
Analytics
- Authors: Neha Bhargava and Fabio Cuzzolin
- Abstract summary: Sport analytics deals with understanding and discovering patterns from a corpus of sports data.
This paper mainly focuses on some the challenges and opportunities presented by sport video analysis in computer vision.
- Score: 6.144873990390373
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we explore some of the applications of computer vision to
sports analytics. Sport analytics deals with understanding and discovering
patterns from a corpus of sports data. Analysing such data provides important
performance metrics for the players, for instance in soccer matches, that could
be useful for estimating their fitness and strengths. Team level statistics can
also be estimated from such analysis. This paper mainly focuses on some the
challenges and opportunities presented by sport video analysis in computer
vision. Specifically, we use our multi-camera setup as a framework to discuss
some of the real-life challenges for machine learning algorithms.
Related papers
- OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videos [56.393522913188704]
We introduce OSL-ActionSpotting, a Python library that unifies different action spotting algorithms to streamline research and applications in sports video analytics.
We successfully integrated three cornerstone action spotting methods into OSL-ActionSpotting, achieving performance metrics that match those of the original, disparates.
arXiv Detail & Related papers (2024-07-01T13:17:37Z) - Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex
and Professional Sports [90.79212954022218]
We introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task.
Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions.
We propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering.
arXiv Detail & Related papers (2024-01-03T02:22:34Z) - A Survey of Advanced Computer Vision Techniques for Sports [0.0]
We build a model for shot speed estimation with pose data obtained using only Computer Vision models.
The proposed methodology is easily replicable for many technical movements.
arXiv Detail & Related papers (2023-01-18T15:01:36Z) - 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) - 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) - Optical tracking in team sports [0.0]
We provide a basic understanding for quantitative data analysts about the process of creating the input data.
We discuss the preprocessing steps of tracking, the most common challenges in this domain, and the application of tracking data to sports teams.
arXiv Detail & Related papers (2022-04-08T15:51:35Z) - 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) - EventAnchor: Reducing Human Interactions in Event Annotation of Racket
Sports Videos [26.516909452362455]
EventAnchor is a data analysis framework to facilitate interactive annotation of racket sports video.
Our approach uses machine learning models in computer vision to help users acquire essential events from videos.
arXiv Detail & Related papers (2021-01-13T09:32:05Z) - 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.