Human Fall Detection via Shape Analysis on Riemannian Manifolds with Applications to Elderly Care
Paper in proceedings, 2015

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.

Human fall detection

assisted living

shape analysis

Riemannian manifolds

elderly care

Author

Yixiao Yun

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE International Conference on Image Processing ICIP, 27-30 Sept., 2015

1522-4880 (ISSN)

3280-3284

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ICIP.2015.7351410

ISBN

978-1-4799-8339-1

More information

Created

10/8/2017