@InProceedings{HuaytaClúaGuér:2021:NoHuHy,
               author = "Huayta, Felix Oliver Sumari and Cl{\'u}a, Esteban Walter Gonzales 
                         and Gu{\'e}rin, Joris",
          affiliation = "{Universidade Federal Fluminense - Instituto de 
                         Computa{\c{c}}{\~a}o} and {Universidade Federal Fluminense - 
                         Instituto de Computa{\c{c}}{\~a}o} and {Universit{\'e} de 
                         Toulouse}",
                title = "A Novel Human-Machine Hybrid Framework for Person 
                         Re-Identification from Full Frame Videos",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Security Application, Person Re-Identification, Pedestrian 
                         Detection.",
             abstract = "With the major adoption of automation for cities security, person 
                         re-identification (Re-ID) has been extensively studied. In this 
                         dissertation, we argue that the current way of studying person 
                         re-identification, i.e. by trying to re-identify a person within 
                         already detected and pre-cropped images of people, is not 
                         sufficient to implement practical security applications, where the 
                         inputs to the system are the full frames of the video streams. To 
                         support this claim, we introduce the Full Frame Person Re-ID 
                         setting and define specific metrics to evaluate FF-PRID 
                         implementations. To improve robustness, we also formalize the 
                         hybrid human-machine collaboration framework, which is inherent to 
                         any Re-ID security applications. To demonstrate the importance of 
                         considering the FF-PRID setting, we build an experiment showing 
                         that combining a good people detection network with a good Re-ID 
                         model does not necessarily produce good results for the final 
                         application. This underlines a failure of the current formulation 
                         in assessing the quality of a Re-ID model and justifies the use of 
                         different metrics. We hope that this work will motivate the 
                         research community to consider the full problem in order to 
                         develop algorithms that are better suited to real-world 
                         scenarios.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45E5JRH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45E5JRH",
           targetfile = "PAPER_SIBGRAPI_2021_CAMERA_READY.pdf",
        urlaccessdate = "2024, May 05"
}