Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46779
DC FieldValueLanguage
dc.contributor.authorSánchez-Nielsen, Elenaen_US
dc.contributor.authorHernandez-Tejera, Marioen_US
dc.date.accessioned2018-11-23T08:05:33Z-
dc.date.available2018-11-23T08:05:33Z-
dc.date.issued2011en_US
dc.identifier.issn1047-3203en_US
dc.identifier.urihttp://hdl.handle.net/10553/46779-
dc.description.abstractMany vision problems require fast and accurate tracking of objects in dynamic scenes. These problems can be formulated as exploration problems and thus can be expressed as a search into a state space based approach. However, these problems are hard to solve because they involve search through a space of transformations corresponding to all the possible motion and deformation. In this paper, we propose a heuristic algorithm through the space of transformations for computing target 2D motion. Three features are combined in order to compute efficient motion: (1) a quality of function match based on a holistic similarity measurement, (2) Kullback–Leibler measure as heuristic to guide the search process and (3) incorporation of target dynamics into the search process for computing the most promising search alternatives. Once 2D motion has been calculated, the result value of the quality of function match computed is used with the purpose of verifying template updates. A template will be updated only when the target object has evolved to a transformed shape dissimilar with respect to the actual shape. Also, a short-term memory subsystem is included with the purpose of recovering previous views of the target object. The paper includes experimental evaluations with video streams that illustrate the efficiency and suitability for real-time vision based tasks in unrestricted environments.en_US
dc.languageengen_US
dc.relationTecnicas Para El Robustecimiento de Procesos en Vision Artificial Para la Interaccionen_US
dc.relation.ispartofJournal of Visual Communication and Image Representationen_US
dc.sourceJournal of Visual Communication and Image Representation [ISSN 1047-3203], v. 22 (6), p. 465-478en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherHeuristic searchen_US
dc.subject.otherHeuristic tracking algorithmen_US
dc.subject.otherKullback-Leibleren_US
dc.subject.otherReal-time visual trackingen_US
dc.subject.otherTarget motionen_US
dc.subject.otherTemplate matchingen_US
dc.subject.otherTemplate trackingen_US
dc.subject.otherTemplate updatingen_US
dc.titleHeuristic algorithm for visual tracking of deformable objectsen_US
dc.typeinfo:eu-repo/semantics/articlees
dc.typeArticlees
dc.identifier.doi10.1016/j.jvcir.2011.05.005en_US
dc.identifier.scopus79960554179-
dc.identifier.isi000294394000002-
dc.contributor.authorscopusid13105159100-
dc.contributor.authorscopusid55966875800-
dc.identifier.eissn1095-9076-
dc.description.lastpage478-
dc.identifier.issue6-
dc.description.firstpage465-
dc.relation.volume22-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.identifier.ulpgces
dc.description.sjr0,595
dc.description.jcr1,122
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.project.principalinvestigatorLorenzo Navarro, José Javier-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-9717-8048-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameHernández Tejera, Francisco Mario-
Appears in Collections:Artículos
Show simple item record

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.