Fall Detection System for Elderly People using Vision-Based Analysis

Fall is one the major cause of death for older people. Detecting the fall plays a major role in saving lives. There are three different types of fall detection commonly used, such as wearable, ambient sensor and vision-based methods. This paper presents a real-time vision-based fall detection system to support the elderly people through analyzing the rate of change of motion with respect to the ground point. The aim of our work is to provide an efficient method to detect fall, without wearing any physical devices. The proposed method is a combination of ground point estimation based on texture segmentation using Gabor filter and calculates the rate of change of angle. A persons movement is tracked by using a Kalman filter and calculates the angle between the tracked points with respect to a ground point. For experimental analysis, we used two public datasets and analyzed parameters. Key-words: Fall detection, Kalman Filter, Gabor Filter Segmentation, Image Processing.