%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.22.23.07
%2 sid.inpe.br/sibgrapi/2016/07.22.23.07.43
%@doi 10.1109/SIBGRAPI.2016.037
%T A new Approach for Dynamic Gesture Recognition using Skeleton Trajectory Representation and Histograms of Cumulative Magnitudes
%D 2016
%A Cardenas, Edwin Jonathan Escobedo,
%A Chávez, Guillermo Cámara,
%@affiliation Federal University of Ouro Preto
%@affiliation Federal University of Ouro Preto
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K hand gesture recognition, spherical coordinate system, keyframes, global and local features, direction cosines, histogram of cumulative magnitudes.
%X 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.
%@language en
%3 PID4373341.pdf