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@InProceedings{MoraisCampPáduCarc:2005:PaFiPr,
               author = "Morais, Erikson Freitas de and Campos, Mario Fernando Montenegro 
                         and P{\'a}dua, Fl{\'a}vio Luis Cardeal and Carceroni, Rodrigo 
                         Lima",
          affiliation = "{Departamento de Ci{\^e}ncia da Computa{\c{c}}{\~a}o - 
                         Universidade Federal de Minas Gerais.} and {Instituto DOCTUM}",
                title = "Particle filter-based predictive tracking for robust fish 
                         counting",
            booktitle = "Proceedings...",
                 year = "2005",
               editor = "Rodrigues, Maria Andr{\'e}ia Formico and Frery, Alejandro 
                         C{\'e}sar",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "tracking, particle filter, fish counting, BraMBle.",
             abstract = "In this paper we study the use of computer vision techniques for 
                         for underwater visual tracking and counting of fishes in vivo. The 
                         methodology is based on the application of a Bayesian filtering 
                         technique that enables tracking of objects whose number may vary 
                         over time. Unlike existing fish-counting methods, this approach 
                         provides adequate means for the acquisition of relevant 
                         information about characteristics of different fish species such 
                         as swimming ability, time of migration and peak flow rates. The 
                         system is also able to estimate fish trajectories over time, which 
                         can be further used to study their behaviors when swimming in 
                         regions of interest. Our experiments demonstrate that the proposed 
                         method can operate reliably under severe environmental changes 
                         (e.g. variations in water turbidity) and handle problems such as 
                         occlusions or large inter-frame motions. The proposed approach was 
                         successfully validated with real-world video streams, achieving 
                         overall accuracy as high as 81%.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "paduaf_fishcounting.pdf",
        urlaccessdate = "2020, Dec. 05"
}


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