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相关实验视频

Updated: Jun 18, 2025

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step

Published on: June 16, 2021

网球运动员行动数据集用于人类姿势估计.

Chun-Yi Wang¹, Kalin Guanlun Lai², Hsu-Chun Huang³

¹Office of Physical Education, National Taichung University of Science and Technology, Taichung, Taiwan.

|July 29, 2024

概括

这项研究引入了一个新的网球数据集用于人类姿势估计. 它使人工智能模型能够训练和验证关键的网球运动,如前手和后手.

关键词:

可可可可可可可可可可可可可可可可可可可可可可可可人类姿势识别功能人类姿势识别关键点检测检测关键点检测位置估计位置估计运动技术技术运动技术网球活动网球活动

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Last Updated: Jun 18, 2025

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科学领域:

运动科学运动科学运动科学
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

在体育训练中整合技术对于提高表现至关重要.
深度学习的进步显著提高了人类姿势估计能力.
现有的数据集可能无法充分覆盖与体育有关的特定运动.

研究的目的:

创建一个全面的数据集,用于训练网球中人类姿势估计模型.
促进开发人工智能驱动的工具,用于网球运动员的分析和训练.
支持体育科学和计算机视觉领域的研究.

主要方法:

收集了个人打网球的视频.
将视频细分成,并注释关键的网球运动.
使用COCO-Annotator进行人类骨关节标签,生成JSON文件.
将注释数据与分类图像结合起来,形成最终的数据集.

主要成果:

开发了一套新的数据集,其中包含标记的网球动作.
数据集包括对前,后,准备位置和服务的注释.
该数据集适用于训练和验证像OpenPose这样的深度学习模型.

结论:

创建的数据集是网球界和人类姿势估计研究的宝贵资源.
这一数据集可以推动体育训练和绩效分析人工智能应用的创新.
它为开发更复杂的网球教练和分析工具提供了基础.