Human fall detection in videos via boosting and fusing statistical features of appearance, shape and motion dynamics on Riemannian manifolds with applications to assisted living

Abstract

This paper addresses issues in fall detection from videos. It is commonly observed that a falling person undergoes large appearance change, shape deformation and physical displacement, thus the focus here is on the analysis of these dynamic features that vary drastically in camera views while a person falls onto the ground. A novel approach is proposed that performs such analysis on Riemannian manifolds, detecting falls from a single camera with arbitrary view angles. The main novelties of this paper include: (a) representing the dynamic appearance, shape and motion of a target person each being points moving on a different Riemannian manifold; (b) characterizing the dynamics of different features by computing velocity statistics of their corresponding manifold points, based on geodesic distances; (c) employing a feature weighting approach, where each statistical feature is weighted according to the mutual information; (d) fusing statistical features learned from different manifolds with a two-stage ensemble learning strategy under a boosting framework. Experiments have been conducted on two video datasets for fall detection. Tests, evaluations and comparisons with 6 state-of-the-art methods have provided support to the effectiveness of the proposed method.