@InProceedings{MirandaViMaLeViCa:2012:ReGeRe,
               author = "Miranda, Leandro and Vieira, Thales and Martinez, Dimas and 
                         Lewiner, Thomas and Vieira, Ant{\^o}nio W. and Campos, Mario F. 
                         M.",
          affiliation = "Mathematics, UFAL  and Mathematics, UFAL  and Mathematics, UFAL  
                         and Mathematics, PUC-Rio  and Computer Science, UFMG  and Computer 
                         Science, UFMG",
                title = "Real-time gesture recognition from depth data through key poses 
                         learning and decision forests",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno, 
                         Roberto and Silva, Luciano",
         organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Gesture recognition , Pose identification , Depth sensors , 3d 
                         motion , Natural user interface.",
             abstract = "Human gesture recognition is a challenging task with many 
                         applications. The popularization of real time depth sensors even 
                         diversifies potential applications to end-user natural user 
                         interface (NUI). The quality of such NUI highly depends on the 
                         robustness and execution speed of the gesture recognition. This 
                         work introduces a method for real-time gesture recognition from a 
                         noisy skeleton stream, such as the ones extracted from Kinect 
                         depth sensors. Each pose is described using a tailored angular 
                         representation of the skeleton joints. Those descriptors serve to 
                         identify key poses through a multi-class classifier derived from 
                         Support Vector learning machines. The gesture is labeled 
                         on-the-fly from the key pose sequence through a decision forest, 
                         that naturally performs the gesture time warping and avoids the 
                         requirement for an initial or neutral pose. The proposed method 
                         runs in real time and shows robustness in several experiments.",
  conference-location = "Ouro Preto, MG, Brazil",
      conference-year = "22-25 Aug. 2012",
                  doi = "10.1109/SIBGRAPI.2012.44",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2012.44",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3C8G5FH",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3C8G5FH",
           targetfile = "gesture_learning_sibgrapi_certified.pdf",
        urlaccessdate = "2024, May 02"
}