Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

TLDR

With an aging population, developed countries must safeguard elderly at home, and computer vision offers a promising way to detect unusual events such as falls. The paper proposes a method to detect falls by analyzing human shape deformation in video. The method tracks the person’s silhouette with a shape‑matching technique, quantifies shape deformation, and uses a Gaussian mixture model to distinguish falls from normal activities. On a realistic dataset of daily activities and simulated falls, the approach achieved near‑zero error with a multi‑camera setup, outperforming other common image‑processing methods.

Abstract

Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.