@InProceedings{SalesVareSchwChav:2018:SiPeRe,
               author = "Sales, Anderson Lu{\'{\i}}s Cavalcanti and Vareto, Rafael 
                         Henrique and Schwartz, William Robson and Chavez, Guillermo 
                         Camara",
          affiliation = "{Universidade Federal de Ouro Preto} and Smart Sense Laboratory, 
                         Department of Computer Science, Universidade Federal de Minas 
                         Gerais and Smart Sense Laboratory, Department of Computer Science, 
                         Universidade Federal de Minas Gerais and {Universidade Federal de 
                         Ouro Preto}",
                title = "Single-Shot Person Re-Identification Combining Similarity Metrics 
                         and Support Vectors",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "re-ID, Person re-identification, handcrafted, CUHK01, PRID450s, 
                         Support Vectors, Similarity Metrics, single-shot.",
             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.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00039",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00039",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RP5PUB",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RP5PUB",
           targetfile = "Paper ID 81.pdf",
        urlaccessdate = "2024, May 05"
}