Intelligent video surveillance for monitoring fall detection of elderly in home environments

TLDR

Video surveillance is widely used to enhance security and safety in intelligent homes, and falls pose a major health hazard for the elderly, motivating monitoring of human activities. The study proposes a novel posture‑based event detection method for elderly monitoring in home surveillance, focusing on fall detection. The method segments the human silhouette, fits an approximated ellipse, computes projection histograms and head‑position changes, extracts feature vectors, and feeds them to an MLP neural network for motion classification and fall detection. Experimental results show a reliable recognition rate, indicating satisfactory performance of the system.

Abstract

Video surveillance is an omnipresent topic when it comes to enhancing security and safety in the intelligent home environments. In this paper, we propose a novel method to detect various posture-based events in a typical elderly monitoring application in a home surveillance scenario. These events include normal daily life activities, abnormal behaviors and unusual events. Due to the fact that falling and its physical-psychological consequences in the elderly are a major health hazard, we monitor human activities with a particular interest to the problem of fall detection. Combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head position, would provide a useful cue for detection of different behaviors. Extracted feature vectors are fed to a MLP neural network for precise classification of motions and determination of fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system.