关于体育应用中的深度学习的调查:感知,理解和决策
IEEE transactions on visualization and computer graphics
|March 26, 2025
概括
深度学习通过感知,理解和决策显著提高体育表现. 这项调查探讨了将深度学习 (DL) 应用于体育方面的算法,数据集和挑战.
科学领域:
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习 (DL) 在体育表现分析中提供了变革性的潜力.
- 应用范围包括体育环境中的感知,理解和决策.
研究的目的:
- 提供关于体育表现中的深度学习的综合调查.
- 分析算法,数据集,虚拟环境和挑战.
- 确定该领域的未来趋势.
主要方法:
- 对层次深度学习算法 (感知,理解,决策) 的审查和比较.
- 编目和评估现有的体育数据集,并指出其特点和局限性.
- 综合当前的挑战和未来的研究方向.
主要成果:
- 深度学习算法根据其在体育表现中的作用进行分类.
- 关键数据集被确定为它们各自的优缺点.
- 概述了体育深度学习中的重大挑战和新兴趋势.
结论:
- 深度学习是提高体育表现的关键技术.
- 了解算法,数据和挑战对于未来的研究至关重要.
- 这项调查是体育AI研究人员的宝贵资源.
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