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使用合成的二维骨数据集进行视角不可知的泰拳道行动识别
Chenglong Luo1, Sung-Woo Kim2, Hun-Young Park2,3
1Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了一种使用3D卷积神经网络的泰拳道poomsae客观评估方法. 该模型确保在不同观点之间一致的行动认可,提高了泰拳道比赛的公平性.
科学领域:
- 计算机科学 计算机科学
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
背景情况:
- 由于缺乏标准化的方法,泰拳道的评价缺乏客观性.
- 不一致的得分和公平性问题源于泰拳道中主观的判断.
- 当前的评估方法与观点的变化作斗争,影响准确性.
研究的目的:
- 开发一个客观的评估系统,使用人工智能对泰拳道的poomsae.
- 为了提高泰拳道Poomsae得分的一致性和公平性.
- 为了创建一个强大的动作识别模型,泰拳道可以适应各种观点.
主要方法:
- 使用三维 (3D) 卷积神经网络 (CNN) 进行动作识别.
- 采用全身运动捕捉套装收集泰拳道的3D骨架数据.
- 从多个角度生成合成的2D骨架,以创建多样化的训练数据集.
主要成果:
- 拟议的3D CNN模型在识别泰拳道的poomsae动作方面表现出了卓越的性能.
- 该模型实现了强大而一致的识别,无论摄像头的视角如何.
- 对2D骨架和RGB图像的性能评估证实了该模型的有效性.
结论:
- 基于3D CNN的模型提供了一种客观和可靠的方法来评估泰拳道的Poomsae.
- 这种人工智能驱动的方法可以显著提高泰拳道比赛的公平性和一致性.
- 该模型的视角不变识别功能解决了自动化体育分析的关键挑战.
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