Vision-Based Fall Detection Using Body Geometry

Abstract

Falling is a major health problem that causes thousands of deaths every year, according to the World Health Organization. Fall detection and fall prediction are both important tasks that should be performed efficiently to enable accurate medical assistance to vulnerable population whenever required. This allows local authorities to predict daily health care resources and reduce fall damages accordingly. We present in this paper a fall detection approach that explores human body geometry available at different frames of the video sequence. Especially, the angular information and the distance between the vector formed by the head -centroid of the identified facial image- and the center hip of the body, and the vector aligned with the horizontal axis of the center hip, are then used to construct distinctive image features. A two-class SVM classifier is trained on the newly constructed feature images, while a Long Short-Term Memory (LSTM) network is trained on the calculated angle and distance sequences to classify falls and non-falls activities. We perform experiments on the Le2i fall detection dataset and the UR FD dataset. The results demonstrate the effectiveness and efficiency of the developed approach.