Human action recognition based on radon transform

Chen, Y; Wu, Q; He, X

Human action recognition based on radon transform

Chen, Y Wu, Q

He, X

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Publication Type:: Chapter
Citation:: 2011, 346 pp. 369 - 389
Issue Date:: 2011-05-09

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Field	Value	Language
dc.contributor.author	Chen, Y	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2011-05-09	en_US
dc.identifier.citation	2011, 346 pp. 369 - 389	en_US
dc.identifier.isbn	9783642195501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17749
dc.description.abstract	A new feature description is used for human action representation and recognition. Features are extracted from the Radon transforms of silhouette images. Using the features, key postures are selected. Key postures are combined to construct an action template for each action sequence. Linear Discriminant Analysis (LDA) is applied to obtain low dimensional feature vectors. Different classification methods are used for human action recognition. Experiments are carried out based on a publicly available human action database. © 2011 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-642-19551-8_13	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Human action recognition based on radon transform	en_US
dc.type	Chapter
utslib.citation.volume	346	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 - CRIN - Realtime Information Networks
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.publication-status	Published	en_US
pubs.volume	346	en_US

Abstract:

A new feature description is used for human action representation and recognition. Features are extracted from the Radon transforms of silhouette images. Using the features, key postures are selected. Key postures are combined to construct an action template for each action sequence. Linear Discriminant Analysis (LDA) is applied to obtain low dimensional feature vectors. Different classification methods are used for human action recognition. Experiments are carried out based on a publicly available human action database. © 2011 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/17749