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dc.contributor.authorHuerta Casado, Iván
dc.contributor.authorHolte, Michael B.
dc.contributor.authorMoeslund, Thomas
dc.contributor.authorGonzàlez, Jordi
dc.date.accessioned2016-03-15T09:49:28Z
dc.date.available2017-07-04T00:30:27Z
dc.date.issued2015
dc.identifier.citationHuerta, I., Holte, M., Moeslund, T., Gonzàlez, J. Chromatic shadow detection and tracking for moving foreground segmentation. "Image and vision computing", 2015, vol. 41, núm. September, p. 42-53.
dc.identifier.issn0262-8856
dc.identifier.urihttp://hdl.handle.net/2117/84360
dc.description.abstractAdvanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently, umbra shadow regions are usually detected as part of moving objects, thus affecting the performance of the final detection. In this paper we address the detection of both penumbra and umbra shadow regions. First, a novel bottom-up approach is presented based on gradient and colour models, which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence, those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution refines even further the segmentation results: a tracking-based top-down approach increases the performance of our bottom-up chromatic shadow detection algorithm by properly correcting non-detected shadows. To do so, a combination of motion filters in a data association framework exploits the temporal consistency between objects and shadows to increase the shadow detection rate. Experimental results exceed current state-of-the-art in shadow accuracy for multiple well-known surveillance image databases which contain different shadowed materials and illumination conditions.
dc.format.extent12 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subject.lcshComputer vision
dc.subject.othercomputer vision. detecting moving objects
dc.subject.otherchromatic shadow detection
dc.subject.othertemporal local gradient
dc.subject.otherspatial and temporal brightness and angle distortions
dc.subject.othershadow tracking
dc.titleChromatic shadow detection and tracking for moving foreground segmentation
dc.typeArticle
dc.subject.lemacVisió per ordinador
dc.identifier.doi10.1016/j.imavis.2015.06.003
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0262885615000712
dc.rights.accessOpen Access
drac.iddocument17659191
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/605598/EU/Cargo handling by Automated Next generation Transportation Systems for ports and terminals/CARGO-ANTS
upcommons.citation.authorHuerta, I., Holte, M., Moeslund, T., Gonzàlez, J.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameImage and vision computing
upcommons.citation.volume41
upcommons.citation.numberSeptember
upcommons.citation.startingPage42
upcommons.citation.endingPage53


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