Machine Learning Techniques For Acquiring New Knowledge in Image Tracking

Rodriguez, B; Concha, OP; Garcia, J; Molina, JM

Machine Learning Techniques For Acquiring New Knowledge in Image Tracking

Rodriguez, B Concha, OP Garcia, J Molina, JM

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Publisher:: Taylor & Francis Inc
Publication Type:: Journal Article
Citation:: Applied Artificial Intelligence, 2008, 22 (3), pp. 266 - 282
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Rodriguez, B	en_US
dc.contributor.author	Concha, OP	en_US
dc.contributor.author	Garcia, J	en_US
dc.contributor.author	Molina, JM	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Applied Artificial Intelligence, 2008, 22 (3), pp. 266 - 282	en_US
dc.identifier.issn	0883-9514	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15180
dc.description.abstract	The purpose of this research is to apply data mining (DM) to an optimized surveillance video system with the objective of improving tracking robustness and stability. Specifically, the machine learning has been applied to blob extraction and detection, in order to decide whether a detected blob corresponds to a real target or not. Performance is assessed with an Evaluation function, which has been developed for optimizing the video surveillance system. This Evaluation function measures the quality level reached by the tracking system.	en_US
dc.publisher	Taylor & Francis Inc	en_US
dc.relation.ispartof	Applied Artificial Intelligence	en_US
dc.relation.isbasedon	10.1080/08839510701821652	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Machine Learning Techniques For Acquiring New Knowledge in Image Tracking	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	22	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	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 Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	3	en_US
pubs.volume	22	en_US

Abstract:

The purpose of this research is to apply data mining (DM) to an optimized surveillance video system with the objective of improving tracking robustness and stability. Specifically, the machine learning has been applied to blob extraction and detection, in order to decide whether a detected blob corresponds to a real target or not. Performance is assessed with an Evaluation function, which has been developed for optimizing the video surveillance system. This Evaluation function measures the quality level reached by the tracking system.

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

http://hdl.handle.net/10453/15180