Fusion of Data from Multiple Cameras for Fall Detection
Jana Machajdik, Sebastian Zambanini, Martin Kampel · 2010
In the context of ambient assisted living, camera-based fall detectors at elderly homes leads to immediate alarming and helping. In this paper we propose a novel approach for the detection of falls based on multiple cameras. Based on semantic driven features fall detection is done in 2D and each camera decides on its own, if a fall has occurred. Fuzzy logic is both used to estimate confidence values for a fall/no fall in the single cameras as well as in the final voting step where the individual decisions are fused to an overall decision. Emphasis is given on simplicity, low computational effort and fast processing. We demonstrate the method and give results on 73 video sequences.