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		<doi>10.1109/SIBGRAPI.2018.00039</doi>
		<citationkey>SalesVareSchwChav:2018:SiPeRe</citationkey>
		<title>Single-Shot Person Re-Identification Combining Similarity Metrics and Support Vectors</title>
		<format>On-line</format>
		<year>2018</year>
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		<author>Sales, Anderson Luís Cavalcanti,</author>
		<author>Vareto, Rafael Henrique,</author>
		<author>Schwartz, William Robson,</author>
		<author>Chavez, Guillermo Camara,</author>
		<affiliation>Universidade Federal de Ouro Preto</affiliation>
		<affiliation>Smart Sense Laboratory, Department of Computer Science, Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Smart Sense Laboratory, Department of Computer Science, Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal de Ouro Preto</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>dersonluis@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<date>29 Oct.-1 Nov. 2018</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>re-ID, Person re-identification, handcrafted, CUHK01, PRID450s, Support Vectors, Similarity Metrics, single-shot.</keywords>
		<abstract>Person Re-Identification is all about determining a person's entire course as s/he walks around camera-equipped zones. More precisely, person Re-ID is the problem of matching human identities captured from non-overlapping surveillance cameras. In this work, we propose an approach that learns a new low-dimensional metric space in an attempt to cut down multi-camera matching errors. We represent the training and test samples by concatenating handcrafted features. Then, the method performs a two-step ranking using elementary distance metrics and followed by an ensemble of weighted binary classifiers. We validate our approach on CUHK01 and PRID450s datasets, providing only a sample per class for probe and only a sample for gallery (single-shot). According to the experiments, our method achieves CMC Rank-1 results up to 61.1 and 75.4, following leading literature protocols, for CUHK01 and PRID450s, respectively.</abstract>
		<language>en</language>
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		<usergroup>dersonluis@gmail.com</usergroup>
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