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Falls are one of the major health risks for the elderly in public health care issues, thus fall detection is particularly necessary for the seniors. In this paper, we propose a novel computer vision-based fall activity detection framework which is based on a new feature extraction method of human motion. We detect falls by analyzing the motion process of human body rather than conventional human shape analysis. Approximate ellipse is first adopted to fit human silhouette, and the areas outside the ellipse are removed to confirm human body silhouette (HBS). Then HBS is divided into three regions, and the motion features are extracted from the centroid coordinate changes of each region. A directed acyclic graph support vector machine (DAG-SVM) classifier is finally applied to detect falls from normal daily activities. The advantage of the proposed method is that it can not only effectively distinguish falls from fall-like activities but also detect the falls parallel to camera optical axis. Experimental results show that our method achieves a state-of-the-art accuracy in fall detection. This method is lightweight and thus can be integrated into embedded devices for real-time health surveillance.

A vision-based fall detection framework for the elderly in a room environment using motion features and DAG-SVM