Joint action segmentation and classification by an extended hidden markov model

Borzeshi, EZ; Concha, OP; Xu, RYD; Piccardi, M

Joint action segmentation and classification by an extended hidden markov model

Borzeshi, EZ Concha, OP Xu, RYD

Piccardi, M

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Publication Type:: Journal Article
Citation:: IEEE Signal Processing Letters, 2013, 20 (12), pp. 1207 - 1210
Issue Date:: 2013-11-04

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Field	Value	Language
dc.contributor.author	Borzeshi, EZ	en_US
dc.contributor.author	Concha, OP	en_US
dc.contributor.author	Xu, RYD https://orcid.org/0000-0003-2080-4762	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2013-11-04	en_US
dc.identifier.citation	IEEE Signal Processing Letters, 2013, 20 (12), pp. 1207 - 1210	en_US
dc.identifier.issn	1070-9908	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26693
dc.description.abstract	Hidden Markov models (HMMs) provide joint segmentation and classification of sequential data by efficient inference algorithms and have therefore been employed in fields as diverse as speech recognition, document processing, and genomics. However, conventional HMMs do not suit action segmentation in video due to the nature of the measurements which are often irregular in space and time, high dimensional and affected by outliers. For this reason, in this paper we present a joint action segmentation and classification approach based on an extended model: the hidden Markov model for multiple, irregular observations (HMM-MIO). Experiments performed over a concatenated version of the popular KTH action dataset and the challenging CMU multi-modal activity dataset (CMU-MMAC) report accuracies comparable to or higher than those of a bag-of-features approach, showing the usefulness of improved sequential models for joint action segmentation and classification tasks. © 1994-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Signal Processing Letters	en_US
dc.relation.isbasedon	10.1109/LSP.2013.2284196	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Joint action segmentation and classification by an extended hidden markov model	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	20	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	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 Electrical and Data Engineering
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 - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

Hidden Markov models (HMMs) provide joint segmentation and classification of sequential data by efficient inference algorithms and have therefore been employed in fields as diverse as speech recognition, document processing, and genomics. However, conventional HMMs do not suit action segmentation in video due to the nature of the measurements which are often irregular in space and time, high dimensional and affected by outliers. For this reason, in this paper we present a joint action segmentation and classification approach based on an extended model: the hidden Markov model for multiple, irregular observations (HMM-MIO). Experiments performed over a concatenated version of the popular KTH action dataset and the challenging CMU multi-modal activity dataset (CMU-MMAC) report accuracies comparable to or higher than those of a bag-of-features approach, showing the usefulness of improved sequential models for joint action segmentation and classification tasks. © 1994-2012 IEEE.

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http://hdl.handle.net/10453/26693