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Proceedings Paper

Human fall detection based on block matching and silhouette area
Author(s): Mariem Gnouma; Ridha Ejbali; Mourad Zaied
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Paper Abstract

Currently, there are several fall detection systems based on video analysis. However, these systems have not yet reached the desired level of appropriateness and robustness. To reduce the risk of falling in insecure environments, a new method is developed in this paper to detect and predict human fall detection. We adopt, in this approach, a Block Matching motion estimation algorithm based on acceleration and changes of the human body silhouette area, which are obtained from a single surveillance camera. It presents an algorithm to accelerate the fall detection system on based on a local adjustment of the velocity field.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034105 (17 March 2017); doi: 10.1117/12.2268988
Show Author Affiliations
Mariem Gnouma, Univ. de Sfax (Tunisia)
Ridha Ejbali, Univ. de Sfax (Tunisia)
Mourad Zaied, Univ. de Sfax (Tunisia)

Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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