Machine Learning Techniques For Acquiring New Knowledge in Image Tracking

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dc.contributor.author Rodriguez, B
dc.contributor.author Concha, OP
dc.contributor.author Garcia, J
dc.contributor.author Molina, JM
dc.date.accessioned 2012-02-02T09:59:50Z
dc.date.issued 2008-01
dc.identifier.citation Applied Artificial Intelligence, 2008, 22 (3), pp. 266 - 282
dc.identifier.issn 0883-9514
dc.identifier.other C1UNSUBMIT 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.
dc.publisher Taylor & Francis Inc
dc.relation.isbasedon 10.1080/08839510701821652
dc.title Machine Learning Techniques For Acquiring New Knowledge in Image Tracking
dc.type Journal Article
dc.parent Applied Artificial Intelligence
dc.journal.volume 3
dc.journal.volume 22
dc.journal.number 3 en_US
dc.publocation PA, USA en_US
dc.identifier.startpage 266 en_US
dc.identifier.endpage 282 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 104828
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.description.keywords NA
dc.description.keywords NA
dc.description.keywords NA
dc.description.keywords NA
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems


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