Automated detection of hands and objects in egocentric videos for ambient assisted living applications

Nguyen, Thi Hoa Cuc, Nebel, Jean-Christophe, Hunter, Gordon and Florez Revuelta, Francisco (2018) Automated detection of hands and objects in egocentric videos for ambient assisted living applications. In: UNSPECIFIED, (ed.) 2018 International Conference on Intelligent Environments : IE 2018. Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. pp. 91-94. ISSN (print) 2469-8792 ISBN 9781538668443

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Abstract

The need for technology assisted (or ambient assisted) living is increasing all the time as the population ages and the number of people with dementia and other conditions impairing memory and cognitive ability increases. In such applications, amongst others, it is necessary to identify and assess potentially hazardous situations. These include scenarios involving a person’s hands and their interactions with various objects. In this paper, we describe our novel approach to identify human hands and objects in videos of people performing a variety of everyday tasks. We compare the performance of our method using different strategies with that of other state of the art approaches. We conclude that, when the proposed approach takes advantage of a pre-trained model, hand detection is performed accurately (94%), providing reliable information for assisted living applications.

Item Type: Book Section
Additional Information: Published version of Nguyen, Thi Hoa Cuc, Nebel, Jean-Christophe, Hunter, Gordon and Florez Revuelta, Francisco (2018) Automated detection of hands and objects in egocentric videos for ambient assisted living applications. In 14th International Conference on Intelligent Environments (IE'18); 25-28 Jun 2018, Rome, Italy.
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics
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Depositing User: Gordon Hunter
Date Deposited: 09 Jan 2019 10:02
Last Modified: 09 Jan 2019 10:02
DOI: https://doi.org/10.1109/IE.2018.00022
URI: http://eprints.kingston.ac.uk/id/eprint/41329

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