Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area
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Abstract
Population of old generation is growing in most countries. Many of these seniors are living alone at home. Falling is among the most dangerous events that often happen and may need immediate medical care. Automatic fall detection systems could help old people and patients to live independently. Vision-based systems have advantage over wearable devices. These visual systems extract some features from video sequences and classify fall and normal activities. These features usually depend on camera's view direction. Using several cameras to solve this problem increases the complexity of the final system. In this paper, we propose to use variations in silhouette area that are obtained from only one camera. We use a simple background separation method to find the silhouette. We show that the proposed feature is view invariant. Extracted feature is fed into a support vector machine for classification. Simulation of the proposed method using a publicly available dataset shows promising results.
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