Evolving Parameters of Surveillance Video Systems for Non-overfitted Learning

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dc.contributor.author Concha, OP
dc.contributor.author Garcia, J
dc.contributor.author Berlanga, A
dc.contributor.author Molina, JM
dc.contributor.editor al, FRE
dc.date.accessioned 2012-02-02T02:12:45Z
dc.date.issued 2005-01
dc.identifier.citation Applications of Evolutionary Computing, 2005, 1, pp. 386 - 395
dc.identifier.isbn 978-3-540-25396-9
dc.identifier.other B1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/14266
dc.description.abstract This paper presents an automated method based on Evolution Strategies (ES) for optimizing the parameters regulating video-based tracking systems. It does not make assumptions about the type of tracking system used. The paper proposes an evaluation metric to assess system performance. The illustration of the method is carried out using three very different video sequences in which the evaluation function assesses trajectories of airplanes, cars or baggage-trucks in an airport surveillance application. Firstly, the optimization is carried out by adjusting to individual trajectories. Secondly, the generalization problem (the search for appropriate solutions to general situations avoiding overfitting) is approached considering combinations of trajectories to take into account in the ES optimization. In both cases, the trained system is tested with the rest of trajectories. Our experiments show how, besides an automatic and reliable adjustment of parameters, the optimization strategy of combining trajectories improves the generalization capability of the training system.
dc.publisher Springer
dc.relation.isbasedon 10.1007/978-3-540-32003-6_39
dc.title Evolving Parameters of Surveillance Video Systems for Non-overfitted Learning
dc.type Chapter
dc.parent Applications of Evolutionary Computing
dc.journal.number en_US
dc.publocation Germany en_US
dc.identifier.startpage 386 en_US
dc.identifier.endpage 395 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 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
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
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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