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A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
IEEE Transactions on Visualization and Computer Graphics
|March 26, 2025
Summary
Deep learning significantly enhances sports performance through perception, comprehension, and decision-making. This survey explores algorithms, datasets, and challenges in applying deep learning (DL) to sports.
Area of Science:
- Sports Science
- Artificial Intelligence
- Machine Learning
Background:
- Deep learning (DL) offers transformative potential in sports performance analysis.
- Applications span perception, comprehension, and decision-making in athletic contexts.
Purpose of the Study:
- To provide a comprehensive survey of deep learning in sports performance.
- To analyze algorithms, datasets, virtual environments, and challenges.
- To identify future trends in the field.
Main Methods:
- Review and comparison of hierarchical deep learning algorithms (perception, comprehension, decision).
- Cataloging and evaluation of existing sports datasets, noting characteristics and limitations.
- Synthesis of current challenges and future research directions.
Main Results:
- Deep learning algorithms are categorized by their role in sports performance.
- Key datasets are identified with their respective strengths and weaknesses.
- Significant challenges and emerging trends in sports deep learning are outlined.
Conclusions:
- Deep learning is a pivotal technology for advancing sports performance.
- Understanding algorithms, data, and challenges is crucial for future research.
- This survey serves as a valuable resource for researchers in sports AI.
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