Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features

Piyathilaka, L; Kodagoda, S

Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features

Piyathilaka, L

Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, 2013, pp. 567 - 572
Issue Date:: 2013-08-19

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Field	Value	Language
dc.contributor.author	Piyathilaka, L https://orcid.org/0000-0003-4676-9387	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2013-08-19	en_US
dc.identifier.citation	Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, 2013, pp. 567 - 572	en_US
dc.identifier.isbn	9781467363211	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130338
dc.description.abstract	Ability to recognize human activities will enhance the capabilities of a robot that interacts with humans. However automatic detection of human activities could be challenging due to the individual nature of the activities. In this paper, we present human activity detection model that uses only 3-D skeleton features generated from an RGB-D sensor (Microsoft Kinect TM). To infer the human activities, we implemented Gaussian Mixture Modal (GMM) based Hidden Markov Model(HMM). GM outputs of the HMM were effectively able to capture multimodel nature of 3D positions of each skeleton joint. We test our model in a publicly available data-set that consists of twelve different daily activities performed by four different people. The proposed model recorded recognition recall accuracy of 84% with previously seen people and 78% with previously unseen people. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013	en_US
dc.relation.isbasedon	10.1109/ICIEA.2013.6566433	en_US
dc.title	Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Melbourne	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Ability to recognize human activities will enhance the capabilities of a robot that interacts with humans. However automatic detection of human activities could be challenging due to the individual nature of the activities. In this paper, we present human activity detection model that uses only 3-D skeleton features generated from an RGB-D sensor (Microsoft Kinect TM). To infer the human activities, we implemented Gaussian Mixture Modal (GMM) based Hidden Markov Model(HMM). GM outputs of the HMM were effectively able to capture multimodel nature of 3D positions of each skeleton joint. We test our model in a publicly available data-set that consists of twelve different daily activities performed by four different people. The proposed model recorded recognition recall accuracy of 84% with previously seen people and 78% with previously unseen people. © 2013 IEEE.

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http://hdl.handle.net/10453/23611