@InProceedings{SennaDrumBast:2017:ReEnTr,
               author = "Senna, Pedro and Drummond, Isabela Neves and Bastos, Guilherme 
                         Sousa",
          affiliation = "{Universidade Federal de Itajub{\'a}} and {Universidade Federal 
                         de Itajub{\'a}} and {Universidade Federal de Itajub{\'a}}",
                title = "Real-time ensemble-based tracker with Kalman filter",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Universidade Federal de Itajub{\'a}.",
             abstract = "This work presents an ensemble-based visual object tracker called 
                         KFebT. This method can fuse using a Kalman Filter the result of 
                         several out-of-the box trackers or specialist methods that solve 
                         parts of the problem, like methods that only estimate the target 
                         scale variation. Our purpose in joining multiple trackers is to 
                         take advantage of the different strengths and weaknesses of each 
                         approach. The proposed fusion method is simple and needs no 
                         training; it just needs the tracker output result and a confidence 
                         measure for the result of each tracker. We performed tests on the 
                         Visual Object Tracking Challenge (VOT) 2015 dataset and evaluated 
                         our tracker in terms of expected overlap, accuracy and robustness. 
                         We test our proposed method on combination of two and three 
                         tracking algorithms and the results demonstrate clear improvements 
                         over the trackers used in its composition.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.51",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.51",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFRN5P",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRN5P",
           targetfile = "PID4960379.pdf",
        urlaccessdate = "2024, May 02"
}