Intelligent Video Surveillance for Monitoring Elderly in Home Environments
Arie Hans Nasution, Sabu Emmanuel · 2007
In this paper we propose a novel method to detect and record various posture-based events of interest in a typical elderly monitoring application in a home surveillance scenario. These events include standing, sitting, bending/squatting, side lying and lying toward the camera. The projection histograms of segmented human body silhouette are used as the main feature vector for posture classification. k-nearest neighbor (k-NN) algorithm and evidence accumulation technique is proposed to infer human postures. With this technique we have achieved a robust recognition rate of above 90% and a stable classifier's output. The modified classifier structure also improves greatly the recognition rate of lying toward the camera events as compared to the result of classifier's structure in GHOST (Haritaoglu et al., 1998). Furthermore, we use the speed of fall to differentiate real fall incident and an event where the person is simply lying without falling.