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@InProceedings{QuiritaHappCostFeit:2017:SyTrEn,
               author = "Quirita, Victor Hugo Ayma and Happ, Patrick Nigri and Costa, 
                         Gilson Alexandre Ostwald Pedro da and Feitosa, Raul Queiroz",
          affiliation = "ELECTRICAL ENGINEERING DEPARTMENT, PONTIFICAL CATHOLIC UNIVERSITY 
                         OF RIO DE JANEIRO and ELECTRICAL ENGINEERING DEPARTMENT, 
                         PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO and INFORMATICS 
                         AND COMPUTER SCIENCE DEPARTMENT, STATE UNIVERSITY OF RIO DE 
                         JANEIRO and ELECTRICAL ENGINEERING DEPARTMENT, PONTIFICAL CATHOLIC 
                         UNIVERSITY OF RIO DE JANEIRO",
                title = "Symbiotic tracker ensemble with feedback learning",
            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 = "OBJECT TRACKING, TRACKING FUSION.",
             abstract = "Visual tracking is a challenging task due to a number of factors, 
                         such as occlusions, deformations, illumination variations and 
                         abrupt motion changes present in a video sequence. Generally, 
                         trackers are robust to some of these factors, but do not achieve 
                         satisfactory results when dealing with multiple factors at the 
                         same time. More robust results when multiple factors are present 
                         can be obtained by combining the results of different trackers. In 
                         this paper we propose a multiple tracker fusion method, named 
                         Symbiotic Tracker Ensemble with Feedback Learning (SymTE-FL), 
                         which combines the results of a set of trackers to produce a 
                         unified tracking estimate. The novelty of the method consists in 
                         providing feedback to the individual trackers, so that they can 
                         correct their own estimates, thus improving overall tracking 
                         accuracy. The proposal is validated by experiments conducted upon 
                         a publicly available database. The results show that the proposed 
                         method delivered in average more accurate tracking estimates than 
                         those obtained with individual trackers running independently and 
                         with the original approach.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "2017_SIBGRAPI_VHAQ.pdf",
        urlaccessdate = "2021, Jan. 25"
}


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