Weakly supervised action recognition using implicit shape models

Thi, TH; Cheng, L; Zhang, J; Wang, L; Satoh, S

Weakly supervised action recognition using implicit shape models

Thi, TH Cheng, L Zhang, J

Wang, L Satoh, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2010, pp. 3517 - 3520
Issue Date:: 2010-11-18

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Field	Value	Language
dc.contributor.author	Thi, TH	en_US
dc.contributor.author	Cheng, L	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Satoh, S	en_US
dc.date.issued	2010-11-18	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2010, pp. 3517 - 3520	en_US
dc.identifier.isbn	9780769541099	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29984
dc.description.abstract	In this paper, we present a robust framework for action recognition in video, that is able to perform competitively against the state-of-the-art methods, yet does not rely on sophisticated background subtraction preprocess to remove background features. In particular, we extend the Implicit Shape Modeling (ISM) of [10] for object recognition to 3D to integrate local spatiotemporal features, which are produced by a weakly supervised Bayesian kernel filter. Experiments on benchmark datasets (including KTH [11] and Weizmann [5]) verifies the effectiveness of our approach. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/ICPR.2010.858	en_US
dc.title	Weakly supervised action recognition using implicit shape models	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Istanbul	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we present a robust framework for action recognition in video, that is able to perform competitively against the state-of-the-art methods, yet does not rely on sophisticated background subtraction preprocess to remove background features. In particular, we extend the Implicit Shape Modeling (ISM) of [10] for object recognition to 3D to integrate local spatiotemporal features, which are produced by a weakly supervised Bayesian kernel filter. Experiments on benchmark datasets (including KTH [11] and Weizmann [5]) verifies the effectiveness of our approach. © 2010 IEEE.

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http://hdl.handle.net/10453/29984