Video based fall detection using features of motion, shape and histogram

ALBAWENDI, Suad, LOTFI, Ahmad, POWELL, Heather and APPIAH, Kofi (2018). Video based fall detection using features of motion, shape and histogram. In: PETRA '18 Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference. ACM Press, 529-536.

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Abstract

Falls are one of the greatest risks for the older adults living alone at home. This paper presents a novel visual-based fall detection approach to support independent living for older adults. The proposed approach employs three unique features; motion information, human shape variation and projection histogram to detect a fall. Motion information of a segmented silhouette, which when extracted can provide a useful cue for classifying different behaviours. Also, the projection histogram and variation in human shape can be used to describe human body postures and subsequently fall events. The proposed approach presented here extracts motion information, using best-fit approximated ellipse around the human body and in addition projection histogram features to further improve the accuracy of fall detection. Experimental results are presented and show high fall detection rate of 99.81% with partially occluded video data.

Item Type:	Book Section
Additional Information:	** From Crossref via Jisc Publications Router. ** Licence for VoR version of this article starting on 26-06-2018: http://www.acm.org/publications/policies/copyright_policy#Background
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Identification Number:	https://doi.org/10.1145/3197768.3201539
Page Range:	529-536
SWORD Depositor:	Margaret Boot
Depositing User:	Margaret Boot
Date Deposited:	09 Aug 2018 09:31
Last Modified:	18 Mar 2021 11:15
URI:	https://shura.shu.ac.uk/id/eprint/21723

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