Making accurate measurements of human body motions using only passive, non-interfering sensors such as video is a difficult task with a wide range of applications throughout biomechanics, health, sports and entertainment. The rise of machine learning-based human pose estimation has allowed for impressive performance gains, but machine learning-based systems require large datasets which might not be practical for niche applications. As such, it may be necessary to adapt systems trained for more general-purpose goals, but this might require a sacrifice in accuracy when compared with systems specifically developed for the application. This paper proposes two approaches to measuring a sprinter’s foot-ground contact locations and timing (step length and step frequency), a task which requires high accuracy. The first approach is a learning-free system based on occupancy maps. The second approach is a multi-camera 3D fusion of a state-of-the-art machine learning-based human pose estimation model. Both systems use the same underlying multi-camera system. The experiments show the learning-free computer vision algorithm to provide foot timing to better than 1 frame at 180 fps, and step length accurate to 7 mm, while the system based on pose estimation achieves timing better than 1.5 frames at 180 fps, and step length estimates accurate to 20 mm.

仅使用无源、无干扰传感器（如视频）对人体运动进行准确测量是一项艰巨的任务，在生物力学、健康、运动和娱乐领域有着广泛的应用。基于机器学习的人体姿态估计的兴起带来了令人印象深刻的性能提升，但基于机器学习的系统需要大型数据集，这对于利基应用可能不实用。因此，可能有必要调整针对更通用目标而训练的系统，但与专门为应用程序开发的系统相比，这可能需要牺牲准确性。本文提出了两种测量短跑运动员脚与地面接触位置和计时（步长和步频）的方法，这是一项需要高精度的任务。第一种方法是基于占用图的免学习系统。第二种方法是最先进的基于机器学习的人体姿态估计模型的多相机 3D 融合。两个系统都使用相同的底层多相机系统。实验表明，免学习计算机视觉算法在 180 fps 下提供足部计时优于 1 帧，步长精确到 7 mm，而基于姿态估计的系统在 180 fps 下实现了优于 1.5 帧的计时，并且步长精确到 7 mm。长度估计精确到 20 毫米。