A Flexible Relational Feature Model for Fall Detection

Andreas Zweng, Thomas Rittler, and Martin Kampel

Keywords

Object Detection, Pedestrian Detection, Ambient Assisted Living

Abstract

Vision based fall detection solutions support elderly who live alone in their homes. For people falling down and not being able to stand up on their own again, such a fall is a major risk. In this work, we show a person detection approach using a relational feature model using consumer depth cameras. We propose a flexible relational feature model (FRFM) for fall detection in combination with histogram similarity functions such as the bhattacharyya distance, histogram intersection, histogram correlation and the chi-square x2 histogram similarity function. FRFM is an extension of the relational feature model (RFM) with the advantage that it can be used for all rotations of a body. The extension is necessary for fall detection due to the fact, that a lying person is rotated in the image where the standard person detection approach detects upright standing persons only. The relational features are computed for verification situations during fall detection on the basis of the Histograms of Oriented Gradients (HOG) feature descriptor. The experimental results show the best setup parameters for our feature model with different types of images (RGB and depth) and results on the specific field of fall detection.

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