Extracting key postures using radon transform

Chen, Y; Wu, Q; He, X; Du, C; Yang, J

Extracting key postures using radon transform

Chen, Y Wu, Q

He, X

Du, C Yang, J

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Publication Type:: Conference Proceeding
Citation:: 2008 23rd International Conference Image and Vision Computing New Zealand, IVCNZ, 2008
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.contributor.author	Du, C	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	2008 23rd International Conference Image and Vision Computing New Zealand, IVCNZ, 2008	en_US
dc.identifier.isbn	9781424425822	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10947
dc.description.abstract	Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video based on Radon transform. Cluster is used on the Radon transform to select the final key postures of human action video. The approach does not require motion extraction from the human action video. The experiments results show that the proposed approach is efficient. © 2008 IEEE.	en_US
dc.relation.ispartof	2008 23rd International Conference Image and Vision Computing New Zealand, IVCNZ	en_US
dc.relation.isbasedon	10.1109/IVCNZ.2008.4762106	en_US
dc.title	Extracting key postures using radon transform	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Christchurch, New Zealand	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 Systems, Management and Leadership
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

Abstract:

Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video based on Radon transform. Cluster is used on the Radon transform to select the final key postures of human action video. The approach does not require motion extraction from the human action video. The experiments results show that the proposed approach is efficient. © 2008 IEEE.

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http://hdl.handle.net/10453/10947