Subspace analysis methods plus motion history image for human action recognition

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

Subspace analysis methods plus motion history image for human action recognition

Du, C Wu, Q

Yang, J He, X

Chen, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, 2008, pp. 606 - 611
Issue Date:: 2008-01-01

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Field	Value	Language
dc.contributor.author	Du, C	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Chen, Y	en_US
dc.date.issued	2008-01-01	en_US
dc.identifier.citation	Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, 2008, pp. 606 - 611	en_US
dc.identifier.isbn	9780769534565	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11922
dc.description.abstract	This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008	en_US
dc.relation.isbasedon	10.1109/DICTA.2008.20	en_US
dc.title	Subspace analysis methods plus motion history image for human action recognition	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Canberra, Australia	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:

This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method. © 2008 IEEE.

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http://hdl.handle.net/10453/11922