Mean-shift background image modelling

Piccardi, M; Jan, T

Mean-shift background image modelling

Piccardi, M

Jan, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2004, 2 pp. 3399 - 3402
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.contributor.author	Jan, T	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2004, 2 pp. 3399 - 3402	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2494
dc.description.abstract	Background modelling is widely used in computer vision for the detection of foreground objects in a frame sequence. The more accurate the background model, the more correct is the detection of the foreground objects. In this paper, we present an approach to background modelling based on a mean-shift procedure. The mean shift vector convergence properties enable the system to achieve reliable background modelling. In addition, histogram-based computation and the new concept of local basins of attraction allow us to meet the stringent real-time requirements of video processing. ©2004 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0452657
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.title	Mean-shift background image modelling	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Singapore City, Singapore	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Background modelling is widely used in computer vision for the detection of foreground objects in a frame sequence. The more accurate the background model, the more correct is the detection of the foreground objects. In this paper, we present an approach to background modelling based on a mean-shift procedure. The mean shift vector convergence properties enable the system to achieve reliable background modelling. In addition, histogram-based computation and the new concept of local basins of attraction allow us to meet the stringent real-time requirements of video processing. ©2004 IEEE.

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http://hdl.handle.net/10453/2494