Reconstructing and analyzing periodic human motion from stationary monocular views

We have shown previously that it is possible to accurately reconstruct periodic motions in 3D from a single camera view, using periodicity as a physical constraint from which to perform geometric inference. In this paper we explore the suitability of the reconstruction techniques for real human motion. We examine the degree of periodicity of human gait empirically, and develop algorithmic tools to address some of the challenges arising from this type of motion, including reconstructing motions that deviate from pure periodicity, properly handling the trajectories of multiple points on an articulated body, and proposing a distance function for measuring the difference between two reconstructions. Importantly, we illustrate the usefulness of these techniques by applying them to the tasks of view-invariant activity classification, clinical gait analysis and person identification.