Joint detection and estimation of multiple objects from image observations

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dc.contributor.authorVo, B.
dc.contributor.authorVo, B.
dc.contributor.authorPham, N.
dc.contributor.authorSuter, D.
dc.date.issued2010
dc.description.abstractThe problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap. These results provide tractable means to jointly estimate the number of states and their values from image observations. As an application, we develop a multi-object filter suitable for image observations with low signal-to-noise ratio (SNR). A particle implementation of the multi-object filter is proposed and demonstrated via simulations.
dc.description.statementofresponsibilityBa-Ngu Vo, Ba-Tuong Vo, Nam-Trung Pham, and David Suter
dc.identifier.citationIEEE Transactions on Signal Processing, 2010; 58(10):5129-5141
dc.identifier.doi10.1109/TSP.2010.2050482
dc.identifier.issn1053-587X
dc.identifier.issn1941-0476
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]
dc.identifier.urihttp://hdl.handle.net/2440/64048
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0880553
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0989007
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0989007
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0880553
dc.rights© 2010 IEEE
dc.source.urihttps://doi.org/10.1109/tsp.2010.2050482
dc.subjectRandom sets
dc.subjectMulti-Bernoulli
dc.subjectprobability hypothesis density (PHD)
dc.subjectfiltering
dc.subjectimages, tracking
dc.subjecttrack before detect (TBD).
dc.titleJoint detection and estimation of multiple objects from image observations
dc.typeJournal article
pubs.publication-statusPublished

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