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Vision-based fall detection for elderly people using body parts movement and shape analysis
Author(s): Chadia Khraief; Faouzi Benzarti ; Hamid Amiri
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Paper Abstract

Falls are the major cause of serious injuries and even death for elderly people. Fall detectors are usually based on wearable devices such as gyroscope, accelerometers, etc. Unfortunately, elderly people often forget to wear them especially those with dementia. In this paper, we present a new vision-based method for automatic fall detection in smart home environment. First, we extract efficiency the person silhouette based on background subtraction method and active contour. Then, motion and shape features are extracted from person body parts and analyzed in order to classify fall from other daily activities using rule-based classification. Evaluation results demonstrate the effectiveness of the proposed method in smart home environment.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410K (15 March 2019); doi: 10.1117/12.2522906
Show Author Affiliations
Chadia Khraief, Ecole Nationale d'Ingénieurs de Tunis (Tunisia)
Faouzi Benzarti , Ecole Supérieure des Communications de Tunis (Tunisia)
Hamid Amiri, Ecole Nationale d'Ingénieurs de Tunis (Tunisia)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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