Abstract
Fall detection is important for safety for old people or patients living alone. This paper proposes a framework for indoor fall detection using single camera system. Falls are detected based on the analysis of motion orientation, motion magnitude, and human shape changes. With a deep analysis of characteristics of fall events, we propose improvements for motion orientation estimation, large motion detection and human shape detection using motion histogram images (MHI). Fall detection is then determined by analyzing the speed of changing in motion magnitude, motion orientation and human shape before, during and after the fall. Experiments have been conducted on public datasets Li2e having 221 videos of different living environments with various daily activities. The experimental results show high detection accuracies and very fast processing capability.
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