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Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorCastanedo, Federico
dc.contributor.authorGarcía, Jesús
dc.contributor.authorPatricio Guisado, Miguel Ángel
dc.contributor.authorMolina, José M.
dc.date.accessioned2010-09-30T08:59:03Z
dc.date.available2010-09-30T08:59:03Z
dc.date.issued2010-07
dc.description13 pages, 12 figures.
dc.description.abstractIn this paper we present a Cooperative Surveillance Multi-Agent System (CS-MAS) architecture extended to incorporate dynamic coalition formation. We illustrate specific coalition formation using fusion skills. In this case, the fusion process is divided into two layers: (i) a global layer in the fusion center, which initializes the coalitions and (ii) a local layer within coalitions, where a local fusion agent is dynamically instantiated. There are several types of autonomous agent: surveillance–sensor agents, a fusion center agent, a local fusion agent, interface agents, record agents, planning agents, etc. Autonomous agents differ in their ability to carry out a specific surveillance task. A surveillance–sensor agent controls and manages individual sensors (usually video cameras). It has different capabilities depending on its functional complexity and limitations related to sensor-specific aspects. In the work presented here we add a new autonomous agent, called the local fusion agent, to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion. The local fusion agent is dynamically created by the fusion center agent and involves several surveillance–sensor agents working in a coalition. We show how the inclusion of this new dynamic local fusion agent guarantees that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions.
dc.description.sponsorshipThis work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505 /TIC/0255 and DPS2008-07029-C02-02.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationInformation Fusion, 2010, v. 11, n. 3, p. 243-255
dc.identifier.doi10.1016/j.inffus.2009.09.002
dc.identifier.issn1566-2535
dc.identifier.publicationfirstpage243
dc.identifier.publicationissue3
dc.identifier.publicationlastpage255
dc.identifier.publicationtitleInformation Fusion
dc.identifier.publicationvolume11
dc.identifier.urihttps://hdl.handle.net/10016/9328
dc.language.isoeng
dc.publisherElsevier
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.inffus.2009.09.002
dc.rights© Elsevier
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherSoftware agents
dc.subject.otherData Fusion
dc.subject.otherCoordination
dc.subject.otherSensor network architecture
dc.subject.otherDistributed vision
dc.subject.otherSurveillance application
dc.titleData fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture
dc.typeresearch article*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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