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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15753
Title: An enhanced triplet CNN based on body parts for person re-identificacion
Authors: Durand Espinoza, Jonathan
Camara Chavez, Guillermo
Hinojosa Torres, Geraldine
Keywords: Computers;Camera view;Feature representation;Human bodies;Improve performance;Overfitting;Partial occlusions;Person re identifications;State of the art;Computer science
Issue Date: 2018
Publisher: IEEE Computer Society
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050964708&doi=10.1109%2fSCCC.2017.8405126&partnerID=40&md5=9e042531aae808dde5cdb698314f43ea
Abstract: Person 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.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15753
ISBN: 9781538634837
ISSN: 15224902
Appears in Collections:Artículos de investigación

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