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Authors: Daniele Liciotti 1 ; Emanuele Frontoni 1 ; Primo Zingaretti 1 ; Nicola Bellotto 2 and Tom Duckett 2

Affiliations: 1 Università Politecnica delle Marche, Italy ; 2 University of Lincoln, United Kingdom

ISBN: 978-989-758-222-6

Keyword(s): ADLs, Human Activity Recognition, HMMs.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Human-Computer Interaction ; Image and Video Analysis ; Learning of Action Patterns ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Physiological Computing Systems ; Software Engineering ; Video Analysis

Abstract: Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D i mages and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available. (More)

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Paper citation in several formats:
Liciotti D., Frontoni E., Zingaretti P., Bellotto N. and Duckett T. (2017). HMM-based Activity Recognition with a Ceiling RGB-D Camera.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 567-574. DOI: 10.5220/0006202305670574

@conference{icpram17,
author={Daniele Liciotti and Emanuele Frontoni and Primo Zingaretti and Nicola Bellotto and Tom Duckett},
title={HMM-based Activity Recognition with a Ceiling RGB-D Camera},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={567-574},
publisher={ScitePress},
organization={INSTICC},
doi={10.5220/0006202305670574},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - HMM-based Activity Recognition with a Ceiling RGB-D Camera
SN - 978-989-758-222-6
AU - Liciotti D.
AU - Frontoni E.
AU - Zingaretti P.
AU - Bellotto N.
AU - Duckett T.
PY - 2017
SP - 567
EP - 574
DO - 10.5220/0006202305670574

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