@InProceedings{AlcantaraPedr:2016:HuAcId,
               author = "Alcantara, Marlon Fernandes de and Pedrini, H{\'e}lio",
          affiliation = "{Universidade Estadual de Campinas} and {Universidade Estadual de 
                         Campinas}",
                title = "Human Action Identification in Videos using Descriptor with 
                         Autonomous Fragments and Multilevel Prediction",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "action recognition, machine learning, computer vision.",
             abstract = "Recent technological advances have provided devices with high 
                         processing power and storage capacities. Video cameras are found 
                         in several places, such as banks, airports, schools, supermarkets, 
                         streets, homes and industries. However, most of the video analysis 
                         tasks are still performed by human operators influenced by factors 
                         such stress and fatigue. This work proposes and evaluates a 
                         methodology for identifying common human actions by means of a 
                         CMSIP descriptor applied to a multilevel prediction scheme with 
                         retraining. The approach is built by dividing the descriptor into 
                         portions considered and interpreted independently by following 
                         distinct ways on the classification model, such that, a central 
                         mechanism will be responsible for deciding which action is being 
                         observed. Our method has proved to be fast and with accuracy 
                         compatible to the state-of-the-art on known public data sets. 
                         Furthermore, the developed prototype demonstrated to be a 
                         promising tool for real-time applications.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M92PCE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M92PCE",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, Apr. 28"
}