@InProceedings{CardenasCháv:2016:NeApDy,
               author = "Cardenas, Edwin Jonathan Escobedo and Ch{\'a}vez, Guillermo 
                         C{\'a}mara",
          affiliation = "{Federal University of Ouro Preto} and {Federal University of Ouro 
                         Preto}",
                title = "A new Approach for Dynamic Gesture Recognition using Skeleton 
                         Trajectory Representation and Histograms of Cumulative 
                         Magnitudes",
            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 = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "hand gesture recognition, spherical coordinate system, keyframes, 
                         global and local features, direction cosines, histogram of 
                         cumulative magnitudes.",
             abstract = "In this paper, we present a new approach for dynamic hand gesture 
                         recognition that uses intensity, depth, and skeleton joint data 
                         captured by Kinect sensor. This method integrates global and local 
                         information of a dynamic gesture. First, we represent the skeleton 
                         3D trajectory in spherical coordinates. Then, we select the most 
                         relevant points in the hand trajectory with our proposed method 
                         for keyframe detection. After, we represent the joint movements by 
                         spatial, temporal and hand position changes information. Next, we 
                         use the direction cosines definition to describe the body 
                         positions by generating histograms of cumulative magnitudes from 
                         the depth data which were converted in a point-cloud. We evaluate 
                         our approach with different public gesture datasets and a sign 
                         language dataset created by us. Our results outperformed 
                         state-of-the-art methods and highlight the smooth and fast 
                         processing for feature extraction being able to be implemented in 
                         real time.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.037",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.037",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5KNG8",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5KNG8",
           targetfile = "PID4373341.pdf",
        urlaccessdate = "2024, Apr. 27"
}