Human action recognition by Radon transform

Chen, Y; Wu, Q; He, X

Human action recognition by Radon transform

Chen, Y Wu, Q

He, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, 2008, pp. 862 - 868
Issue Date:: 2008-12-01

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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	2008-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, 2008, pp. 862 - 868	en_US
dc.identifier.isbn	9780769535036	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10838
dc.description.abstract	A new feature description is used for human behaviour representation and recognition. The feature is based on Radon transforms of extracted silhouettes. Key postures are selected based on the Radon transform. Key postures are combined to construct an action template for each sequence. Linear Discriminant Analysis (LDA) is applied to the set of key postures to obtain low dimensional feature vectors. Different classification methods are used to classify each sequence. Experiments are carried out based on a publically available human behaviour database and the results are exciting. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008	en_US
dc.relation.isbasedon	10.1109/ICDMW.2008.26	en_US
dc.title	Human action recognition by Radon transform	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Pisa, Italy	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	open_access
pubs.publication-status	Published	en_US

Abstract:

A new feature description is used for human behaviour representation and recognition. The feature is based on Radon transforms of extracted silhouettes. Key postures are selected based on the Radon transform. Key postures are combined to construct an action template for each sequence. Linear Discriminant Analysis (LDA) is applied to the set of key postures to obtain low dimensional feature vectors. Different classification methods are used to classify each sequence. Experiments are carried out based on a publically available human behaviour database and the results are exciting. © 2008 IEEE.

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http://hdl.handle.net/10453/10838