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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2014  >  43(1): 60-65

ZHANG Yi, ZHANG Shuo, LUO Yuan. View-Invariant 3D Hand Trajectory-Based Recognition[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(1): 60-65. doi: 10.3969/j.issn.1001-0548.2014.01.010
Citation: ZHANG Yi, ZHANG Shuo, LUO Yuan. View-Invariant 3D Hand Trajectory-Based Recognition[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(1): 60-65. doi: 10.3969/j.issn.1001-0548.2014.01.010
shu

View-Invariant 3D Hand Trajectory-Based Recognition

doi: 10.3969/j.issn.1001-0548.2014.01.010
  • 1.

    Institute of Automation,Chongqing University of Posts and Telecommunications Shapingba Chongqing 400065;

  • 2.

    Institute of Optoelectronics Engineering,Chongqing University of Posts and Telecommunications Shapingba Chongqing 400065

  • Received Date: 2012-10-15
  • Rev Recd Date: 2013-04-11
  • Publish Date: 2014-02-15
  • This paper proposes a novel method for view-invariant 3D hand trajectory-based recognition. The image depth information in gesture segmentation is collected by using Kinect sensor. View-invariant 3D hand trajectory is represented by improving centroid distance function. Hidden Markov model is applied to train and recognize hand gesture. Experiment results show that the proposed method is robust under the condition of different illumination and complex background. The illustrated system can successfully recognize spotted hand gestures with a 97.7% recognition rate for Arabic numbers 0 to 9.
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  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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View-Invariant 3D Hand Trajectory-Based Recognition

doi: 10.3969/j.issn.1001-0548.2014.01.010
  • 1. Institute of Automation,Chongqing University of Posts and Telecommunications Shapingba Chongqing 400065;
  • 2. Institute of Optoelectronics Engineering,Chongqing University of Posts and Telecommunications Shapingba Chongqing 400065
  • Received Date: 2012-10-15
  • Rev Recd Date: 2013-04-11
  • Publish Date: 2014-02-15

Abstract: This paper proposes a novel method for view-invariant 3D hand trajectory-based recognition. The image depth information in gesture segmentation is collected by using Kinect sensor. View-invariant 3D hand trajectory is represented by improving centroid distance function. Hidden Markov model is applied to train and recognize hand gesture. Experiment results show that the proposed method is robust under the condition of different illumination and complex background. The illustrated system can successfully recognize spotted hand gestures with a 97.7% recognition rate for Arabic numbers 0 to 9.

ZHANG Yi, ZHANG Shuo, LUO Yuan. View-Invariant 3D Hand Trajectory-Based Recognition[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(1): 60-65. doi: 10.3969/j.issn.1001-0548.2014.01.010
Citation: ZHANG Yi, ZHANG Shuo, LUO Yuan. View-Invariant 3D Hand Trajectory-Based Recognition[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(1): 60-65. doi: 10.3969/j.issn.1001-0548.2014.01.010
shu

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