Histogram-based training initialisation of hidden Markov models for human action recognition

Moghaddam, Z; Piccardi, M

Histogram-based training initialisation of hidden Markov models for human action recognition

Moghaddam, Z Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010, 2010, pp. 256 - 261
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Moghaddam, Z	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010, 2010, pp. 256 - 261	en_US
dc.identifier.isbn	9780769542645	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16157
dc.description.abstract	Human action recognition is often addressed by use of latent-state models such as the hidden Markov model and similar graphical models. As such models require Expectation-Maximisation training, arbitrary choices must be made for training initialisation, with major impact on the final recognition accuracy. In this paper, we propose a histogram-based deterministic initialisation and compare it with both random and a time-based deterministic initialisations. Experiments on a human action dataset show that the accuracy of the proposed method proved higher than that of the other tested methods. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010	en_US
dc.relation.isbasedon	10.1109/AVSS.2010.25	en_US
dc.title	Histogram-based training initialisation of hidden Markov models for human action recognition	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Boston, Massachusetts, USA	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/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Human action recognition is often addressed by use of latent-state models such as the hidden Markov model and similar graphical models. As such models require Expectation-Maximisation training, arbitrary choices must be made for training initialisation, with major impact on the final recognition accuracy. In this paper, we propose a histogram-based deterministic initialisation and compare it with both random and a time-based deterministic initialisations. Experiments on a human action dataset show that the accuracy of the proposed method proved higher than that of the other tested methods. © 2010 IEEE.

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http://hdl.handle.net/10453/16157