Automatic high fidelity foot contact location and timing for elite sprinting

Abstract

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.