乒乓球中冲击分类作为一个多标签分类任务,每个冲击有两个标签
Yuta Fujihara1, Tomoyasu Shimada1, Xiangbo Kong2
1Graduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了乒乓球击球的多标签分类,通过识别球员的姿势和球的动态来提高准确性. 这种方法提高了类似的乒乓球运动的动作识别.
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
- 机器学习 机器学习
背景情况:
- 乒乓球的击球是关键的游戏元素,需要从游戏数据中准确分类.
- 由于不同动作之间高度的视觉相似性,对乒乓球的击球进行分类是具有挑战性的.
- 现有的动作识别模型与乒乓球中风识别的细微差别作斗争.
研究的目的:
- 为桌球开发一种新的多标签冲击分类方法.
- 通过对每次冲击分配多个标签来提高乒乓球冲击识别的准确性.
- 评估多标签和不同输入方式 (视频,3D坐标) 的有效性.
主要方法:
- 提出了一种多标签分类方法,将球员的姿势和球的旋转/速度标签分配给每次击球.
- 修改了动作识别模型,以整合多个输出,以增强中风分类.
- 使用视频数据与3D联合坐标作为输入来比较性能.
主要成果:
- 与单标签方法相比,多标签分类在验证数据上提高了8.6%的准确性,在测试数据上提高了18.1%.
- 使用3D联合坐标作为输入比单独的视频数据更能提高准确度 (17.1%在验证时,5.4%在测试时).
- 多输出方法减少了分类难度,提高了整体准确性.
结论:
- 多标签分类是一种更有效的策略,用于识别类似的乒乓球击球.
- 三维联合坐标是提高乒乓球动作识别精度的有价值的输入方式.
- 拟议的方法在分析和分类乒乓球技巧方面取得了重大进展.
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