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Chun-Ming Chang, Che-Hao Chang, Chung-Lin Huang. Real-Time Hand Motion Parameter Estimation with Feature Point Detection Using Kinect[J]. Journal of Electronic Science and Technology, 2014, 12(4): 429-433. DOI: 10.3969/j.issn.1674-862X.2014.04.017
Citation: Chun-Ming Chang, Che-Hao Chang, Chung-Lin Huang. Real-Time Hand Motion Parameter Estimation with Feature Point Detection Using Kinect[J]. Journal of Electronic Science and Technology, 2014, 12(4): 429-433. DOI: 10.3969/j.issn.1674-862X.2014.04.017
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Real-Time Hand Motion Parameter Estimation with Feature Point Detection Using Kinect

doi: 10.3969/j.issn.1674-862X.2014.04.017

  • Department of Applied Informatics and Multimedia,Asia University,Taichung

Funds: 

This work was supported by NSC under Grand No. 101-2221-E-468-030.

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  • Author Bio:

    Chun-Ming Chang research interests include computer vision/image processing, video compression, virtual reality, computer networks, and robotics, cmchang@ asia.edu.tw;
    Che-Hao Chang, bbb00437@ hotmail.com;
    Chung-Lin Huang, huang.chunglin@gmail.com

    Chun-Ming Chang research interests include computer vision/image processing, video compression, virtual reality, computer networks, and robotics, cmchang@ asia.edu.tw;
    Che-Hao Chang, bbb00437@ hotmail.com;
    Chung-Lin Huang, huang.chunglin@gmail.com

    Chun-Ming Chang research interests include computer vision/image processing, video compression, virtual reality, computer networks, and robotics, cmchang@ asia.edu.tw;
    Che-Hao Chang, bbb00437@ hotmail.com;
    Chung-Lin Huang, huang.chunglin@gmail.com

  • Authors’ information: Chun-Ming Chang
  • Received Date: 2013-12-14
  • Rev Recd Date: 2014-03-14
  • Publish Date: 2014-12-24
    Abstract
  • This paper presents a real-time Kinectbased hand pose estimation method. Different from model-based and appearance-based approaches, our approach retrieves continuous hand motion parameters in real time. First, the hand region is segmented from the depth image. Then, some specific feature points on the hand are located by the random forest classifier, and the relative displacements of these feature points are transformed to a rotation invariant feature vector. Finally, the system retrieves the hand joint parameters by applying the regression functions on the feature vectors. Experimental results are compared with the ground truth dataset obtained by a data glove to show the effectiveness of our approach. The effects of different distances and different rotation angles for the estimation accuracy are also evaluated.
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