Fall Detection using Ceiling-mounted 3D Depth Camera

Michal Kepski, Bogdan Kwolek

2014

Abstract

This paper proposes an algorithm for fall detection using a ceiling-mounted 3D depth camera. The lying pose is separated from common daily activities by a k-NN classifier, which was trained on features expressing headfloor distance, person area and shape’s major length to width. In order to distinguish between intentional lying postures and accidental falls the algorithm also employs motion between static postures. The experimental validation of the algorithm was conducted on realistic depth image sequences of daily activities and simulated falls. It was evaluated on more than 45000 depth images and gave 0% error. To reduce the processing overload an accelerometer was used to indicate the potential impact of the person and to start an analysis of depth images.

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Paper Citation


in Harvard Style

Kepski M. and Kwolek B. (2014). Fall Detection using Ceiling-mounted 3D Depth Camera . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 640-647. DOI: 10.5220/0004742406400647


in Bibtex Style

@conference{visapp14,
author={Michal Kepski and Bogdan Kwolek},
title={Fall Detection using Ceiling-mounted 3D Depth Camera},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={640-647},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004742406400647},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Fall Detection using Ceiling-mounted 3D Depth Camera
SN - 978-989-758-004-8
AU - Kepski M.
AU - Kwolek B.
PY - 2014
SP - 640
EP - 647
DO - 10.5220/0004742406400647