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Interactive multiple object learning with scanty human supervision
dc.contributor.author | Villamizar Vergel, Michael Alejandro |
dc.contributor.author | Garrell Zulueta, Anais |
dc.contributor.author | Sanfeliu Cortés, Alberto |
dc.contributor.author | Moreno-Noguer, Francesc |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2017-03-27T09:48:52Z |
dc.date.available | 2018-08-01T00:30:12Z |
dc.date.issued | 2016-08 |
dc.identifier.citation | Villamizar, M.A., Garrell, A., Sanfeliu, A., Moreno-Noguer, F. Interactive multiple object learning with scanty human supervision. "Computer vision and image understanding", Agost 2016, vol. 149, p. 51-64. |
dc.identifier.issn | 1077-3142 |
dc.identifier.uri | http://hdl.handle.net/2117/102900 |
dc.description | © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.description.abstract | We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier.; We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. (C) 2016 Elsevier Inc. All rights reserved. |
dc.format.extent | 14 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.other | Object recognition |
dc.subject.other | Interactive learning |
dc.subject.other | Online classifier |
dc.subject.other | Human-robot interaction |
dc.subject.other | human-robot interaction |
dc.subject.other | recognition |
dc.title | Interactive multiple object learning with scanty human supervision |
dc.type | Article |
dc.contributor.group | Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents |
dc.contributor.group | Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
dc.identifier.doi | 10.1016/j.cviu.2016.03.010 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.inspec | Classificació INSPEC::Pattern recognition |
dc.subject.inspec | Classificació INSPEC::Automation::Robots |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S1077314216300042 |
dc.rights.access | Open Access |
local.identifier.drac | 18770964 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//DPI2013-42458-P/ES/INTERACCION, APRENDIZAJE Y COOPERACION ROBOT ? HUMANO EN AREAS URBANAS/ |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/644271/EU/AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance/AEROARMS |
local.citation.author | Villamizar, M.A.; Garrell, A.; Sanfeliu, A.; Moreno-Noguer, F. |
local.citation.publicationName | Computer vision and image understanding |
local.citation.volume | 149 |
local.citation.startingPage | 51 |
local.citation.endingPage | 64 |
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