@InProceedings{JordãoSchw:2016:GoFaBe,
               author = "Jord{\~a}o, Artur and Schwartz, William Robson",
          affiliation = "DCC-UFMG and DCC-UFMG",
                title = "The Good, The Fast and The Better Pedestrian Detector",
            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 = "Oblique Decision Tree, Partial Least Squares, Filtering 
                         Approaches, High-Level Information, Fusion of Detectors.",
             abstract = "Pedestrian detection is a well-known problem in Computer Vision, 
                         mostly because of its direct applications in surveillance, transit 
                         safety and robotics. In the past decade, several efforts have been 
                         performed to improve the detection in terms of accuracy, velocity 
                         and enhancement of features. In this work, we proposed and 
                         analyzed techniques focusing on these points. Firstly, we propose 
                         an accurate oblique random forest associated with Partial Least 
                         Squares (PLS). The method consists on utilize the PLS to find a 
                         decision surface at each node in a decision tree. Secondly, we 
                         evaluate filtering approaches to reduce the search space and keep 
                         only potential regions of interest to be presented to detectors, 
                         speeding up the detection process. Finally, we propose a novel 
                         approach to extract powerful features regarding the scene. The 
                         method combines results of distinct pedestrian detectors by 
                         reinforcing the human hypothesis whereas suppressing a significant 
                         number of false positives.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M9GPQ8",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M9GPQ8",
           targetfile = "Main.pdf",
        urlaccessdate = "2024, Apr. 29"
}