Human fall detection via shape analysis on Riemannian manifolds with applications to elderly care
This paper addresses issues in fall detection from videos. The focus is on the analysis of human shapes which deform drastically in camera views while a person falls onto the ground. A novel approach is proposed that performs fall detection from an arbitrary view angle, via shape analysis on a unified Riemannian manifold for different camera views. The main novelties of this paper include: (a) representing dynamic shapes as points moving on a unit n-sphere, one of the simplest Riemannian manifolds; (b) characterizing the deformation of shapes by computing velocity statistics of their corresponding manifold points, based on geodesic distances on the manifold. Experiments have been conducted on two publicly available video datasets for fall detection. Test, evaluations and comparisons with 6 existing methods show the effectiveness of our proposed method.