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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorZuriarrain Arcarazo, Iker
dc.contributor.otherMekonnen, Alhayat Ali
dc.contributor.otherLerasle, Frederic
dc.date.accessioned2024-04-29T13:53:19Z
dc.date.available2024-04-29T13:53:19Z
dc.date.issued2011
dc.identifier.isbn9789898425478en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=154734en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6385
dc.description.abstractThis paper addresses multi-modal person detection and tracking using a 2D SICK Laser Range Finder and a visual camera from a mobile robot in a crowded and cluttered environment. A sequential approach in which the laser data is segmented to filter human leg like structures to generate person hypothesis which are further refined by a state of the art parts based visual person detector for final detection, is proposed. Based on this detection routine, a Monte Carlo Markov Chain (MCMC) particle filtering strategy is utilized to track multiple persons around the robot. Integration of the implemented multi-modal person detector and tracker in our robotic platform and associated experiments are presented. Results obtained from all tests carried out have been clearly reported proving the multi-modal approach outperforms its single sensor counterparts taking detection, subsequent use, computation time, and precision into account. The work presented here will be used to define navigational con trol laws for passer-by avoidance during a service robot’s person following activity.en
dc.language.isoengen
dc.publisherScitepressen
dc.rights© 2011 SCITEPRESSen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMulti-person trackingen
dc.subjectMulti-modal data fusionen
dc.subjectMCMC particle filteringen
dc.subjectInteractive roboticsen
dc.titleMulti-modal person detection and tracking from a mobile robot in a crowded environmenten
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceProceedings of the International Conference on Computer Vision Theory and Application (VISAPP 2011)en
local.description.peerreviewedtrueen
local.identifier.doi10.5220/0003367705110520en
local.contributor.otherinstitutionhttps://ror.org/02feahw73es
local.contributor.otherinstitutionhttps://ror.org/004raaa70es
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Registro sencillo

Attribution-NonCommercial-NoDerivatives 4.0 International
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