Fall detection from depth map video sequences

Authors:
Caroline Rougier;Edouard Auvinet;Jacqueline Rousseau;Max Mignotte;Jean Meunier
Affiliations:
Department of Computer Science and Operations Research, University of Montreal, QC, Canada;Department of Computer Science and Operations Research, University of Montreal, QC, Canada;Research Center of the Geriatric Institute, University of Montreal, QC, Canada;Department of Computer Science and Operations Research, University of Montreal, QC, Canada;Department of Computer Science and Operations Research, University of Montreal, QC, Canada
Venue:
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Year:
2011

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Abstract

Falls are one of the major risks for seniors living alone at home. Computer vision systems, which do not require to wear sensors, offer a new and promising solution for fall detection. In this work, an occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity. Indeed, the first feature is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground. However, this method can fail if the end of the fall is completely occluded behind furniture. Fortunately, these cases can be managed by using the 3D person velocity computed just before the occlusion.