对单眼人体姿势估计模型的评估,用于临床运动分析
David Rode1, Annika Dunkel2, Romina Willi3
1ETH Zurich, D-HEST, Zurich, Switzerland. david.rode@hest.ethz.ch.
Scientific reports
|November 5, 2025
概括
无标记器的人体姿势估计比基于标记器的系统具有优势,但具有精度限制. 这项研究使用新的数据集评估了11种开源方法,发现了体育活动分析的显著性能差异.
科学领域:
- 计算机视觉 计算机视觉
- 生物机械分析 生物机械分析
- 机器学习 机器学习
背景情况:
- 无标记的人体姿势估计 (HPE) 正在成为基于标记的动作捕捉的经济有效的替代方案.
- 虽然提供了诸如低硬件需求和更快的设置等好处,但无标记HPE通常在准确性和速度方面落后.
- 这种性能差距可能会限制无标记技术的应用范围.
研究的目的:
- 综合评估11种开源单眼无标记HPE方法的准确性,精度和推断速度.
- 为了将这些估计器与黄金标准基于光学运动捕捉的标记器进行比较.
- 评估它们对于分析体力活动的适用性.
主要方法:
- 创建了Physio2.2M,这是一个数据集,包含来自25名参与者执行练习的220万张RGB.
- 使用基于标记器的光学运动捕捉系统获得的地面真实数据.
- 在Physio2.2M数据集上对11个单眼无标记HPE算法的评估.
主要成果:
- 每个关节位置的平均误差在72-122mm (2D) 和146-249mm (3D) 之间.
- 膝盖和肘部曲角度显示了[公式:见文本] (2D) 和[公式:见文本] (3D) 的平均绝对误差.
- 推断速度各不相同:直接估计器 (25-200 FPS),二维到三维提升方法 (117-9341 FPS).
结论:
- 性能 (准确度,精度,速度) 在无标记HPE方法和图像尺寸之间有很大的差异.
- 一些2D估计器接近视觉评估的准确性.
- 目前的无标记HPE方法有希望,但在体力活动中对精确的生物力学分析有局限性.
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