Implicit motion-shape model: A generic approach for action matching

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

Implicit motion-shape model: A generic approach for action matching

Thi, TH Cheng, L Zhang, J

Wang, L

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2010, pp. 1477 - 1480
Issue Date:: 2010-12-01

Closed Access

	Filename	Description	Size
	2013006871OK.pdf		1.22 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2010, pp. 1477 - 1480	en_US
dc.identifier.isbn	9781424479948	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29721
dc.description.abstract	We develop a robust technique to find similar matches of human actions in video. Given a query video, Motion History Images (MHI) are constructed for consecutive keyframes. This is followed by dividing the MHI into local Motion-Shape regions, which allows us to analyze the action as a set of sparse space-time patches in 3D. Inspired by the idea of Generalized Hough Transform, we develop the Implicit Motion-Shape Model that allows the integration of these local patches to describe the dynamic characteristics of the query action. In the same way we retrieve motion segments from video candidates, then project them onto the Hough Space built by the query model. This produces the matching score by running Parzen window density estimation under different scales. Empirical experiments on popular datasets demonstrate the efficiency of this approach, where highly accurate matches are returned within acceptable processing time. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2010.5652843	en_US
dc.title	Implicit motion-shape model: A generic approach for action matching	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hong Kong	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:

We develop a robust technique to find similar matches of human actions in video. Given a query video, Motion History Images (MHI) are constructed for consecutive keyframes. This is followed by dividing the MHI into local Motion-Shape regions, which allows us to analyze the action as a set of sparse space-time patches in 3D. Inspired by the idea of Generalized Hough Transform, we develop the Implicit Motion-Shape Model that allows the integration of these local patches to describe the dynamic characteristics of the query action. In the same way we retrieve motion segments from video candidates, then project them onto the Hough Space built by the query model. This produces the matching score by running Parzen window density estimation under different scales. Empirical experiments on popular datasets demonstrate the efficiency of this approach, where highly accurate matches are returned within acceptable processing time. © 2010 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/29721