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.
ACM International Conference Proceeding Series
Volume 08-09-December-2016, 8 December 2016, Pages 339-344
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