Unobtrusive indoor surveillance of patients at home using multiple Kinect sensors

In this paper we propose a system for unobtrusive automated indoor surveillance of subjects in indoor environment using the Kinect sensor. We demonstrate that the features of identity, location and activity of a person can be detected with considerable accuracy using the system. Further, we show how existing design patterns can be used to create a data parallel and scalable architecture for such surveillance in real-time.