  Falls are serious and costly for elderly people. The Centers for Disease
Control and Prevention of the US reports that millions of older people, 65 and
older, fall each year at least once. Serious injuries such as; hip fractures,
broken bones or head injury, are caused by 20% of the falls. The time it takes
to respond and treat a fallen person is crucial. With this paper we present a
new , non-invasive system for fallen people detection. Our approach uses only
stereo camera data for passively sensing the environment. The key novelty is a
human fall detector which uses a CNN based human pose estimator in combination
with stereo data to reconstruct the human pose in 3D and estimate the ground
plane in 3D. Furthermore, our system consists of a reasoning module which
formulates a number of measures to reason whether a person is fallen. We have
tested our approach in different scenarios covering most activities elderly
people might encounter living at home. Based on our extensive evaluations, our
systems shows high accuracy and almost no miss-classification. To reproduce our
results, the implementation is publicly available to the scientific community.
