Enhanced importance sampling: Unscented auxiliary particle filtering for visual tracking

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dc.contributor.authorShen, C.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.authorDick, A.
dc.contributor.authorBrooks, M.
dc.contributor.conferenceAustralian Joint Conference on Artificial Intelligence (17th : 2004 : Cairns, Qld.)
dc.contributor.editorWebb, G.
dc.contributor.editorYu, X.
dc.date.issued2004
dc.descriptionThe original publication is available at www.springerlink.com
dc.description.abstractThe particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particle filter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor performance in visual tracking. It is not a trivial task to design satisfactory proposal distributions for the particle filter. In this paper, we introduce an improved particle filtering framework into visual tracking, which combines the unscented Kalman filter and the auxiliary particle filter. The efficient unscented auxiliary particle filter (UAPF) uses the unscented transformation to predict one-step ahead likelihood and produces more reasonable proposal distributions, thus reducing the number of particles required and substantially improving the tracking performance. Experiments on real video sequences demonstrate that the UAPF is computationally efficient and outperforms the conventional particle filter and the auxiliary particle filter.
dc.description.statementofresponsibilityChunhua Shen, Anton van den Hengel, Anthony Dick and Michael J. Brooks
dc.identifier.citationAI 2004 : advances in artificial intelligence : 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004 : proceedings / Geoffrey I. Webb, Xinghuo Yu (eds.), pp. 180-191
dc.identifier.doi10.1007/b104336
dc.identifier.isbn3540240594
dc.identifier.isbn9783540240594
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]
dc.identifier.orcidDick, A. [0000-0001-9049-7345]
dc.identifier.orcidBrooks, M. [0000-0001-9612-5884]
dc.identifier.urihttp://hdl.handle.net/2440/29538
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeBerlin, Germany
dc.relation.ispartofseriesLecture notes in computer science ; 3339.
dc.source.urihttp://www.springerlink.com/content/3jyxx8h6exjwuygk/
dc.titleEnhanced importance sampling: Unscented auxiliary particle filtering for visual tracking
dc.typeConference paper
pubs.publication-statusPublished

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