An online HDP-HMM for joint action segmentation and classification in motion capture data

Bargi, A; Da Xu, RY; Piccardi, M

An online HDP-HMM for joint action segmentation and classification in motion capture data

Bargi, A Da Xu, RY Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012, pp. 1 - 7
Issue Date:: 2012-08-20

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Field	Value	Language
dc.contributor.author	Bargi, A	en_US
dc.contributor.author	Da Xu, RY	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2012-08-20	en_US
dc.identifier.citation	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012, pp. 1 - 7	en_US
dc.identifier.isbn	9781467316118	en_US
dc.identifier.issn	2160-7508	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31704
dc.description.abstract	Since its inception, action recognition research has mainly focused on recognizing actions from closed, predefined sets of classes. Conversely, the problem of recognizing actions from open, possibly incremental sets of classes is still largely unexplored. In this paper, we propose a novel online method based on the "sticky" hierarchical Dirichlet process and the hidden Markov model [11, 5]. This approach, labelled as the online HDP-HMM, provides joint segmentation and classification of actions while a) processing the data in an online, recursive manner, b) discovering new classes as they occur, and c) adjusting its parameters over the streaming data. In a set of experiments, we have applied the online HDP-HMM to recognize actions from motion capture data from the TUM kitchen dataset, a challenging dataset of manipulation actions in a kitchen [12]. The results show significant accuracy in action classification, time segmentation and determination of the number of action classes. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops	en_US
dc.relation.isbasedon	10.1109/CVPRW.2012.6239230	en_US
dc.title	An online HDP-HMM for joint action segmentation and classification in motion capture data	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Providence RI, USA
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
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:

Since its inception, action recognition research has mainly focused on recognizing actions from closed, predefined sets of classes. Conversely, the problem of recognizing actions from open, possibly incremental sets of classes is still largely unexplored. In this paper, we propose a novel online method based on the "sticky" hierarchical Dirichlet process and the hidden Markov model [11, 5]. This approach, labelled as the online HDP-HMM, provides joint segmentation and classification of actions while a) processing the data in an online, recursive manner, b) discovering new classes as they occur, and c) adjusting its parameters over the streaming data. In a set of experiments, we have applied the online HDP-HMM to recognize actions from motion capture data from the TUM kitchen dataset, a challenging dataset of manipulation actions in a kitchen [12]. The results show significant accuracy in action classification, time segmentation and determination of the number of action classes. © 2012 IEEE.

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http://hdl.handle.net/10453/31704