Evaluating depth-based computer vision methods for fall detection under occlusions

Abstract

Falls are one of the major risks for seniors living alone at home. Fall detection has been widely studied in the computer vision community, especially since the advent of affordable depth sensing technology like the Kinect. Most existing methods assume that the whole fall process is visible to the camera. This is not always the case, however, since the end of the fall can be completely occluded by a certain object, like a bed. For a system to be usable in real life, the occlusion problem must be addressed. To quantify the challenges and assess performance in this topic, we present an occluded fall detection benchmark dataset containing 60 occluded falls for which the end of the fall is completely occluded. We also evaluate four existing fall detection methods using a single depth camera [1–4] on this benchmark dataset.