Fall Detection Using Body Volume Recontruction and Vertical Repartition Analysis

With the life expectancy increase, more and more elderly people risk to fall at home. In order to help them living safely at home by reducing the eventuality of unrescued fall, autonomous systems are developped. In this paper, we propose a new method to detect falls at home, based on a multiple cameras network for reconstructing the 3D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormaly near the floor which implies that a person has fallen on the floor. This method is evaluated regarding the number of cameras (from 3 to 8) with 22 fall scenarios. Results show 96% of correct detections with 3 cameras and above 99% with 4 cameras and more.

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