A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This article presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. First, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Second, 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.
Authors
- Mingli Song (ORCID: https://orcid.org/0000-0003-2621-6048)
- Shidong Cao (ORCID: https://orcid.org/0009-0005-7132-8753)
- Guanhong Wang
- Zhonghan Zhao (ORCID: https://orcid.org/0000-0003-0993-2018)
- Shengyu Hao (ORCID: https://orcid.org/0000-0002-8652-8556)
- Gaoang Wang (ORCID: https://orcid.org/0000-0002-8403-1538)
- Jenq–Neng Hwang (ORCID: https://orcid.org/0000-0002-8877-2421)
- Wenhao Hu (ORCID: https://orcid.org/0009-0006-3279-8733)
- Wenhao Chai (ORCID: https://orcid.org/0000-0003-2611-0008)
Institutions
- University of Illinois Urbana-Champaign (US)
- Zhejiang University of Science and Technology (CN)
- University of Washington (US)
- Zhejiang University-University of Edinburgh Institute (CN)
- Zhejiang University of Technology (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1109/tvcg.2025.3554801
- Citations
- 8
- Primary Topic
- Sports Analytics and Performance
- Type
- article
- Field-Weighted Citation Impact
- 25.05