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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15753
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dc.contributor.authorDurand Espinoza, Jonathan-
dc.contributor.authorCamara Chavez, Guillermo-
dc.contributor.authorHinojosa Torres, Geraldine-
dc.date.accessioned2019-01-29T22:19:48Z-
dc.date.available2019-01-29T22:19:48Z-
dc.date.issued2018-
dc.identifier.isbn9781538634837es_PE
dc.identifier.issn15224902es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15753-
dc.description.abstractPerson re-identificacion consists of reidentificating person through a set of images that is taken by different camera views. Despite recent advances in this field, this problem still remains a challenge due to partial occlusions, changes in illumination, variation in human body poses. In this paper, we present an enhanced Triplet CNN based on body-parts for person re-identification (AETCNN). We design a new model able to learn local body-part features and integrate them to produce the final feature representation of each input person. In addition, to avoid over-fitting due to the small size of the dataset, we propose an improvement in triplet assignment to speed up the convergence and improve performance. Experiments show that our approach achieves very promising results in (CUHK01) dataset and we advance state of the art, improving most of the results of the state of the art with a simpler architecture, achieving 76.50% in rank 1. © 2017 IEEE.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherIEEE Computer Societyes_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85050964708&doi=10.1109%2fSCCC.2017.8405126&partnerID=40&md5=9e042531aae808dde5cdb698314f43eaes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectComputerses_PE
dc.subjectCamera viewes_PE
dc.subjectFeature representationes_PE
dc.subjectHuman bodieses_PE
dc.subjectImprove performancees_PE
dc.subjectOverfittinges_PE
dc.subjectPartial occlusionses_PE
dc.subjectPerson re identificationses_PE
dc.subjectState of the artes_PE
dc.subjectComputer sciencees_PE
dc.titleAn enhanced triplet CNN based on body parts for person re-identificaciones_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.identifier.doi10.1109/SCCC.2017.8405126es_PE
Appears in Collections:Artículos de investigación

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