A real-time fall detection system based on HMM and RVM

Abstract

The growing population of seniors leads to the need for an intelligent surveillance system to ensure the safety of the elders at home. Fall is one kind of the most seriously life-threatening emergencies for elderly people. Fall detection system based on video surveillance provides an efficient solution for detecting fall events automatically by analyzing human behaviors. In this paper, we propose a context-based fall detection system by analyzing human motion and posture using hidden Markov model (HMM) and relevance vector machine (RVM) respectively. Additionally, we integrate homography to deal with falls in any direction. The system is validated on an open fall database and our own video dataset. Experimental results demonstrate that our method achieves high robustness and accuracy in detecting different kinds of falls and runs at a real-time speed.