Kazakh Traditional Dance Gesture Recognition

2014 International Conference on Science & Engineering in Mathematics, Chemistry and Physics

数学;化学;物理学

Nussipbekov, A.K.^1 ; Amirgaliyev, E.N.^1 ; Hahn, Minsoo^2

Faculty of Mechanics and Mathematics, Al-Farabi Kazakh National University, Almaty, Kazakhstan^1

Digital Media Lab, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of^2

关键词: Depth information;    Expectation-maximization algorithms;    Head gesture recognition;    Interdisciplinary research;    K;    means clustering;    Microsoft kinect;    State-of-the-art system;    Tree-structured;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/495/1/012036/pdf
DOI  :  10.1088/1742-6596/495/1/012036

来源: IOP

【 摘 要 】

Full body gesture recognition is an important and interdisciplinary research field which is widely used in many application spheres including dance gesture recognition. The rapid growth of technology in recent years brought a lot of contribution in this domain. However it is still challenging task. In this paper we implement Kazakh traditional dance gesture recognition. We use Microsoft Kinect camera to obtain human skeleton and depth information. Then we apply tree-structured Bayesian network and Expectation Maximization algorithm with K-means clustering to calculate conditional linear Gaussians for classifying poses. And finally we use Hidden Markov Model to detect dance gestures. Our main contribution is that we extend Kinect skeleton by adding headwear as a new skeleton joint which is calculated from depth image. This novelty allows us to significantly improve the accuracy of head gesture recognition of a dancer which in turn plays considerable role in whole body gesture recognition. Experimental results show the efficiency of the proposed method and that its performance is comparable to the state-of-the-art system performances.