Human body articulation for action recognition in video sequences

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

Human body articulation for action recognition in video sequences

Thi, TH Lu, S Zhang, J

Cheng, L Wang, L

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Publication Type:: Conference Proceeding
Citation:: 6th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, 2009, pp. 92 - 97
Issue Date:: 2009-12-28

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Field	Value	Language
dc.contributor.author	Thi, TH	en_US
dc.contributor.author	Lu, S	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Cheng, L	en_US
dc.contributor.author	Wang, L	en_US
dc.date.issued	2009-12-28	en_US
dc.identifier.citation	6th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, 2009, pp. 92 - 97	en_US
dc.identifier.isbn	9780769537184	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29888
dc.description.abstract	This paper presents a new technique for action recognition in video using human body part-based approach, combining both local feature description of each body part, and global graphical model structure of the human action. The human body is divided into elementary points from which a Decomposable Triangulated Graph will be built. The temporal variation of human activity is encoded in the velocity distribution of each node in the graph, while the graph structure shows the spatial configuration of all the nodes in the action. Tracking trajectories of unlabeled good feature points are correctly labeled using Maximum a Posterior probability. Dynamic Programming is then implemented to boost up the exhaustive search for the optimal labeling of unknown body parts and the best possible action. A simple and efficient technique for building the optimal structure of the human action graph is also implemented. Experimental results on the KTH dataset proves the success and potential applications of this proposed technique. © 2009 IEEE.	en_US
dc.relation.ispartof	6th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009	en_US
dc.relation.isbasedon	10.1109/AVSS.2009.41	en_US
dc.title	Human body articulation for action recognition in video sequences	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Genova	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:

This paper presents a new technique for action recognition in video using human body part-based approach, combining both local feature description of each body part, and global graphical model structure of the human action. The human body is divided into elementary points from which a Decomposable Triangulated Graph will be built. The temporal variation of human activity is encoded in the velocity distribution of each node in the graph, while the graph structure shows the spatial configuration of all the nodes in the action. Tracking trajectories of unlabeled good feature points are correctly labeled using Maximum a Posterior probability. Dynamic Programming is then implemented to boost up the exhaustive search for the optimal labeling of unknown body parts and the best possible action. A simple and efficient technique for building the optimal structure of the human action graph is also implemented. Experimental results on the KTH dataset proves the success and potential applications of this proposed technique. © 2009 IEEE.

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http://hdl.handle.net/10453/29888