Detecting fall incidents of the elderly based on human-ground contact areas

People always make a little contact with the ground during usual activities mainly by feet but often lie completely on the ground after accidental falls. Thus, we propose using Human-Ground Contact Areas (HGCA) to classify human states into standing, sitting and lying states. A fall is defined by a fast change of human states from standing or sitting to lying and continuity in lying state for a sufficient duration. Temporal analyzing human-state transitions is used to discriminate falls from usual events. To measure HGCA, we project foreground of monitored person from one view to another by using homography of the ground between them. Overlap regions between projected foreground and foreground in the latter view that only exist in which people are in contact with the ground, due to plane parallax, are measured as HGCA. We generalize a threshold of HGCA to separate lying states from the others from view-invariant distributions of HGCA with respect to human states. We propose using human state simulation in which camera viewpoints are freely changed to capture 3D human models in various states. Hundreds of images are generated from the simulation as training data to build these distributions. We test our method on 'multiple camera fall dataset' leading to a competitive performance with other methods tested on the same dataset.