Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/116293
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dc.contributor.authorZhu, M.en
dc.contributor.authorDick, A.en
dc.contributor.authorvan den Hengel, A.en
dc.date.issued2017en
dc.identifier.citationAdvanced Concepts for Intelligent Vision Systems: proceedings, 2017 / Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (ed./s), vol.10617 LNCS, pp.455-467en
dc.identifier.isbn9783319703527en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.urihttp://hdl.handle.net/2440/116293-
dc.description.abstractThis paper proposes a scalable and robust algorithm to find connections between cameras in a large surveillance network, based solely on lighting variation. We show how to detect regions that are affected by lighting changes within each camera view, with limited data. Then, we establish the light-overlap connections and show that our algorithm can scale to hundreds of camera while maintaining high accuracy. We demonstrate our method on a campus network of 100 real cameras and 500 simulated cameras, and evaluate its accuracy and scalability.en
dc.description.statementofresponsibilityMichael Zhu, Anthony Dick, Anton van den Hengelen
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture Notes in Computer Science; 10617en
dc.rights┬ęSpringer International Publishing AG 2017en
dc.subjectLarge-scale intelligent video surveillance; topology estimation; light-overlap; lighting variation detection; segmentationen
dc.titleLarge-scale camera network topology estimation by lighting variationen
dc.typeConference paperen
dc.identifier.rmid0030079352en
dc.contributor.conference18th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2017) (18 Sep 2017 - 21 Sep 2017 : Antwerp, Belgium)en
dc.identifier.doi10.1007/978-3-319-70353-4_39en
dc.identifier.pubid390427-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS05en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidDick, A. [0000-0001-9049-7345]en
dc.identifier.orcidvan den Hengel, A. [0000-0003-3027-8364]en
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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