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dc.contributor.authorTabb, Ken
dc.contributor.authorGeorge, S.
dc.contributor.authorDavey, N.
dc.contributor.authorAdams, R.G.
dc.date.accessioned2007-10-03T14:36:07Z
dc.date.available2007-10-03T14:36:07Z
dc.date.issued2001
dc.identifier.citationTabb , K , George , S , Davey , N & Adams , R G 2001 , Omni-directional motion: pedestrian shape classification using neural networks and active contour models . in In: Proc. of Image and Vision Computing New Zealand (IVCNZ) . pp. 387-392 , Image and Vision Computing New Zealand (IVCNZ) , Dunedin , New Zealand , 26/11/01 .
dc.identifier.citationconference
dc.identifier.otherPURE: 406615
dc.identifier.otherPURE UUID: 9e628d8c-c75b-4435-a7f4-a30753ae0e28
dc.identifier.otherdspace: 2299/818
dc.identifier.urihttp://hdl.handle.net/2299/818
dc.description.abstractThis paper describes a hybrid vision system which, following initial user interaction, can detect and track objects in the visual field, and classify them as human and non-human. The system incorporates an active contour model for detecting and tracking objects, a method of translating the contours into scale-, location- and resolution-independent vectors, and an error-backpropagation feedforward neural network for shape classification of these vectors. The network is able to generate a confidence value for a given shape, determining how ‘human’ and how ‘non-human’ it considers the shape to be. This confidence value changes as the object moves around, providing a motion signature for an object. Previous work has accommodated lateral pedestrian movement across the visual field; this paper describes a system which accommodates all angles of pedestrian movement on the ground plane.en
dc.language.isoeng
dc.relation.ispartofIn: Proc. of Image and Vision Computing New Zealand (IVCNZ)
dc.subjectsnake
dc.subjectactive contour model
dc.subjectshape classification
dc.subjectneural network
dc.subjectOmni-directional
dc.subjectAxis crossover vector
dc.subjectground plane
dc.titleOmni-directional motion: pedestrian shape classification using neural networks and active contour modelsen
dc.contributor.institutionHealth and Human Sciences Central
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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