Fall Detection using Head Tracking and Centroid Movement Based on a Depth Camera

Abstract

The number of elderly people living alone has increased over the las1t years and fall is one of major risks that threaten their lives. A fall detection system has become a requirement and computer vision is an efficient solution among many accurate solutions developed in this field. This paper proposes a novel method vision-based fall detection using depth camera, which combines human shape analysis, head tracking and centroid detection to validate falls. An experimental test done with SDUFall dataset which contains 20 subjects performing five daily activities and falls demonstrates the efficiency of our method, achieving up to 93.25% accuracy compared with the state-of-the-art method using the same dataset.