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dc.contributor.authorPang, Shaoning
dc.contributor.authorZhao, Jing (Jane)
dc.contributor.authorHartill, B.
dc.contributor.authorSarrafzadeh, Hossein
dc.date.accessioned2017-06-15T00:33:20Z
dc.date.available2017-06-15T00:33:20Z
dc.date.issued2016
dc.identifier.issn2049-887X
dc.identifier.issn2049-8888
dc.identifier.urihttps://hdl.handle.net/10652/3802
dc.description.abstractBackground modelling, used in many vision systems, must be robust to environmental change, yet sensitive enough to identify all moving objects of interest. Existing background modelling approaches have been developed to interpret images in terrestrial situations, such as car parks and stretches of road, where objects move in a smooth manner and the background is relatively consistent. In the context of maritime boat ramps surveillance, this paper proposes a cognitive background modelling method for land and water composition scenes (CBM-lw) to interpret the traffic of boats passing across boat ramps. We compute an adaptive learning rate to account for changes on land and water composition scenes, in which a geometrical model is integrated with pixel classification to determine the portion of water changes caused by tidal dynamics and other environmental influences. Experimental comparative tests and quantitative performance evaluations of real-world boat-flow monitoring traffic sequences demonstrate the benefits of the proposed algorithm.en_NZ
dc.language.isoenen_NZ
dc.publisherInderscience Enterprisesen_NZ
dc.subjectbackground modellingen_NZ
dc.subjectmoving object detectionen_NZ
dc.subjectmarine trafficen_NZ
dc.subjectland and water composition sceneen_NZ
dc.subjectdynamic learning rateen_NZ
dc.titleModelling land water composition scene for maritime traffic surveillanceen_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2017-05-23T14:30:05Z
dc.rights.holderInderscience Enterprisesen_NZ
dc.subject.marsden080110 Simulation and Modellingen_NZ
dc.identifier.bibliographicCitationPang, S., Zhao, J., Hartill, B., & Sarrafzadeh, A. (2016). Modelling land water composition scene for maritime traffic surveillance. International Journal of Applied Pattern Recognition, 3(4), pp.324-350.en_NZ
unitec.publication.spage324en_NZ
unitec.publication.lpage350en_NZ
unitec.publication.volume3en_NZ
unitec.publication.issue4en_NZ
unitec.publication.titleInternational Journal of Applied Pattern Recognitionen_NZ
unitec.peerreviewedyesen_NZ
unitec.identifier.roms59597en_NZ


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