2D articulated tracking with dynamic Bayesian networks

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dc.contributor.authorShen, C.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.authorDick, A.
dc.contributor.authorBrooks, M.
dc.contributor.conferenceInternational Conference on Computer and Information Technology (4th : 2004 : Wuhan, China)
dc.contributor.editorWei, D.
dc.contributor.editorWang, H.
dc.contributor.editorPeng, Z.
dc.contributor.editorKara, A.
dc.contributor.editorHe, Y.
dc.date.issued2004
dc.description©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.description.abstractWe present a novel method for tracking the motion of an articulated structure in a video sequence. The analysis of articulated motion is challenging because of the potentially large number of degrees of freedom (DOFs) of an articulated body. For particle filter based algorithms, the number of samples required with high dimensional problems can be computationally prohibitive. To alleviate this problem, we represent the articulated object as an undirected graphical model (or Markov Random Field, MRF) in which soft constraints between adjacent subparts are captured by conditional probability distributions. The graphical model is extended across time frames to implement a tracker. The tracking algorithm can be interpreted as a belief inference procedure on a dynamic Bayesian network. The discretisation of the state vectors makes it possible to utilise the efficient belief propagation (BP) and mean field (MF) algorithms to reason in this network. Experiments on real video sequences demonstrate that the proposed method is computationally efficient and performs well in tracking the human body.
dc.description.statementofresponsibilityChunhua Shen, Anton van den Hengel, Anthony Dick, Michael J. Brooks
dc.identifier.citationThe Fourth International Conference on Computer and Information Technology : proceedings : September 14-16, 2004, Wuhan, China / Daming Wei (ed.), pp. 130-136
dc.identifier.doi10.1109/CIT.2004.1357185
dc.identifier.isbn0769522165
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/2440/29537
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeLos Alamitos, California, USA
dc.source.urihttps://doi.org/10.1109/cit.2004.1357185
dc.title2D articulated tracking with dynamic Bayesian networks
dc.typeConference paper
pubs.publication-statusPublished

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