Saliency based human fall detection in smart home environments using posture recognition

Abstract

The implementation of people monitoring system is an evolving research theme. This paper introduces an elderly monitoring system that recognizes human posture from overlapping cameras for people fall detection in a smart home environment. In these environments, the zone of movement is limited. Our approach used this characteristic to recognize human posture fastly by proposing a region-wise modelling approach. It classifies persons pose in four groups: standing, crouching, sitting and lying on the floor. These postures are obtained by calculating an estimation of the human bounding volume. This volume is estimated by obtaining the height of the person and its surface that is in contact with the ground according to the foreground information of each camera. Using them, we distinguish each postures and differentiate lying on floor posture, which can be considered as the falling posture from other postures. The global multiview information of the scene is obtaining by using homographic projection. We test our proposed algorithm on multiple cameras based fall detection public dataset and the results prove the efficiency of our method.

Mapping a Decade of Smart Homes for the Elderly in Web of Science: A Scientometric Review in CiteSpace

“…Various technical means are usually used, including visual recognition, accelerometers, and sensors. Among them, optical recognition technology can monitor the activity status of the elderly by installing cameras to detect if they fall and then analysing the images in the cameras through intelligent algorithms to quickly identify the fall events of older adults and issue alerts promptly [57]. In addition, accelerometers and sensor technologies can monitor body posture and action trajectory in real time via sensors on older adults or furnishings.…”