A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

• URL: http://arxiv.org/abs/2307.03353v1
• Date: Fri, 7 Jul 2023 02:22:17 GMT
• Title: A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
• Authors: Zhonghan Zhao, Wenhao Chai, Shengyu Hao, Wenhao Hu, Guanhong Wang, Shidong Cao, Mingli Song, Jenq-Neng Hwang, Gaoang Wang
• Abstract summary: Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper focuses on three main aspects: algorithms, datasets and virtual environments, and challenges.
• Score: 41.427845300209945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications.

Related papers

• Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video [5.885902974241053]
  Reasoning over complex sports scenarios has posed significant challenges to current NLP technologies. Our evaluation spans from simple queries on basic rules and historical facts to complex, context-specific reasoning. We propose a new benchmark based on a comprehensive overview of existing sports datasets and provided extensive error analysis.
  arXiv  Detail & Related papers  (2024-06-21T05:57:50Z)
• SportQA: A Benchmark for Sports Understanding in Large Language Models [12.860652075730876]
  SportQA is a benchmark specifically designed for evaluating Large Language Models (LLMs) in the context of sports understanding. SportQA encompasses over 70,000 multiple-choice questions across three distinct difficulty levels. Our results reveal that while LLMs exhibit competent performance in basic sports knowledge, they struggle with more complex, scenario-based sports reasoning.
  arXiv  Detail & Related papers  (2024-02-24T17:12:10Z)
• 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)
• DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations [3.000319651350124]
  This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings.
  arXiv  Detail & Related papers  (2022-08-17T09:55:02Z)
• 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 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)
• 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)
• Challenges and Opportunities for Computer Vision in Real-life Soccer Analytics [6.144873990390373]
  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.
  arXiv  Detail & Related papers  (2020-04-13T20:06:23Z)
• Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities [52.59080024266596]
  We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets. We then propose a new taxonomy to structure the deep methods by challenges.
  arXiv  Detail & Related papers  (2020-01-21T09:55:59Z)
• A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [60.335974351919816]
  Object perception is a fundamental sub-field of Computer Vision. Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
  arXiv  Detail & Related papers  (2019-12-26T13:26:49Z)

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.