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2016 | OriginalPaper | Buchkapitel

A New Method for Fall Detection of Elderly Based on Human Shape and Motion Variation

verfasst von : Abderrazak Iazzi, Mohammed Rziza, Rachid Oulad Haj Thami, Driss Aboutajdine

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

Fall detection for elderly and patient has been an active research topic due to the great demand for products and technology of fall detection in the healthcare industry. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, we present a new method for fall detection based on the variation of shape and motion. First, we use the CodeBook method to extract the person silhouette from the video. Then, information of rectangle, ellipse and histogram projection are used to provide features to analyze the person shape. In addition, we represent the person shape by three blocks extracted from rectangle. Then, we use optical flow to analyze the person motion within each blocks. Finally, falls are detected from normal activities using thresholding-based method. All experiments show that our fall detection system achieves very good performances in accuracy and error rate.

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Vorheriges Kapitel Unsupervised Deep Networks for Temporal Localization of Human Actions in Streaming Videos
Nächstes Kapitel Motion of Oriented Magnitudes Patterns for Human Action Recognition
Fußnoten
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Metadaten
Titel
A New Method for Fall Detection of Elderly Based on Human Shape and Motion Variation
verfasst von
Abderrazak Iazzi
Mohammed Rziza
Rachid Oulad Haj Thami
Driss Aboutajdine
Copyright-Jahr
2016
Buch
Advances in Visual Computing

Print ISBN: 978-3-319-50831-3

Electronic ISBN: 978-3-319-50832-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50832-0_16