Continuous human action recognition in ambient assisted living scenarios

Andre Chaaraoui, Alexandros and Florez-Revuelta, Francisco (2014) Continuous human action recognition in ambient assisted living scenarios. In: MONAMI 2014 International Conference on Mobile Networks and Management; 22-26 Sep 2014, Wurzburg, Germany.

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Abstract

Ambient assisted living technologies and services make it possible to help elderly and impaired people and increase their personal autonomy. Specifically, vision-based approaches enable the recognition of human behaviour, which in turn allows to build valuable services upon. However, a main constraint is that these have to be able to work online and in real time. In this work, a human action recognition method based on a bag-of-key-poses model and sequence alignment is extended to support continuous human action recognition. The detection of action zones is proposed to locate the most discriminative segments of an action. For the recognition, a method based on a sliding and growing window approach is presented. Furthermore, an evaluation scheme particularly designed for ambient assisted living scenarios is introduced. Experimental results on two publicly available datasets are provided. These show that the proposed action zones lead to a significant improvement and allow real-time processing.

Item Type: Conference or Workshop Item (Paper)
Event Title: MONAMI 2014 International Conference on Mobile Networks and Management
Organising Body: MONAMI
Additional Information: Published in: Ramón Agüero, Thomas Zinner, Rossitza Goleva, Andreas Timm-Giel, Phuoc Tran-Gia (2015), Mobile Networks and Management 6th International Conference, MONAMI 2014, Würzburg, Germany, September 22-26, 2014, Revised Selected Papers, Cham, Springer, pp. 344-357, ISBN: 9783319162911, ISSN: 1867-8211.
Uncontrolled Keywords: ambient assisted living; human action recognition; continuous recognition; action zones; real time
Research Area: Architecture and the built environment
Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017)
Depositing User: Automatic Import Agent
Date Deposited: 15 Nov 2019 14:57
Last Modified: 02 Dec 2019 16:32
DOI: https://doi.org/10.1007/978-3-319-16292-8_25
URI: http://eprints.kingston.ac.uk/id/eprint/34990

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