Advertisement

A Survey on Dynamic Hand Gesture Recognition Using Kinect Device

  • Aamrah Ikram
  • Yue Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

In Human Computer Interface (HCI) technology, Hand Gestures Recognition (HGR) is a diverse field. In Dynamic Hand gesture recognition (DHGR), an unprecedented work has been done over few decades and it is still growing day by day. HGR has been extensively used in other scopes like biomedical, gaming and entertainment, research and monitoring etc. Because of its versatile utility, HGR is getting popular among the people, as it is making HCI more efficient, natural and user friendly. For the purpose of accurate segmentation and tracking a controller free and fascinating device, Kinect was introduced. In this paper Kinect based algorithms are discussed and addressed. Algorithms for DHGR are compared and particularly focused on Hidden Markov Model (HMM) and Support Vector Machine (SVM). At the end, it is observed that recognition accuracy improved significantly with Kinect device due to its good interactive features, efficiency and accuracy.

Keywords

Kinect device Hidden Markov Model Support Vector Machine Gesture recognition Human computer interaction 

References

  1. 1.
    Shrivastava, R.: A hidden Markov model based dynamic hand gesture recognition system using OpenCV. In: 2013 IEEE 3rd International Advance Computing Conference (IACC). IEEE (2013)Google Scholar
  2. 2.
    Ren, Z., Meng, J., Yuan, J.: Depth camera based hand gesture recognition and its applications in human-computer-interaction. In: 2011 8th International Conference on Information, Communications and Signal Processing (ICICS). IEEE (2011)Google Scholar
  3. 3.
    Liu, K., et al.: Fusion of inertial and depth sensor data for robust hand gesture recognition. IEEE Sens. J. 14(6), 1898–1903 (2014)CrossRefGoogle Scholar
  4. 4.
    Tan, W., Wu, C., Zhao, S., Li, J.: Dynamic hand gesture recognition using motion trajectories and key frames. In: 2nd (ICACC) (2010)Google Scholar
  5. 5.
    Lamberti, L., Camastra, F.: Real-time hand gesture recognition using a color glove. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011. LNCS, vol. 6978, pp. 365–373. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24085-0_38CrossRefGoogle Scholar
  6. 6.
    Caputo, M., Denker, K., Dums, B., Umlauf, G.: 3D Hand gesture recognition based on sensor fusion of commodity hardware. In: Mensch and Computer, vol. 2012, pp. 293–302 (2012)Google Scholar
  7. 7.
    Vinh, T.Q., Tri, N.T.: Hand gesture recognition based on depth image using kinect sensor. In: 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (2015)Google Scholar
  8. 8.
    Hazari, S.S., Asaduzzaman: Designing a sign language translation system using kinect motion sensor device. In: International Conference on Electrical, Computer and Communication Engineering (ECCE), Coxs Bazar, Bangladesh, 16–18 February 2017Google Scholar
  9. 9.
    Chen, Y., Luo, B.: A real-time dynamic hand gesture recognition system using kinect sensor. In: Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics, Zhuhai, China, 6–9 December 2015Google Scholar
  10. 10.
    Wang, X., Xia, M., Cai, H., Gao, Y., Cattani, C.: Hidden-Markov-models- based dynamic hand gesture recognition. Math. Prob. Eng. 2012, 1–10 (2012)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Wang, Y., Yang, C., Wu, X.: Kinect based dynamic hand gesture recognition algorithm research. In: 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics (2012)Google Scholar
  12. 12.
    Roccetti, M., Marfia, G., Semeraro, A.: Playing into the wild: a gesture-based interface for gaming in public spaces. J. Vis. Commun. Image Represent. 23(3), 426–440 (2012)CrossRefGoogle Scholar
  13. 13.
    GNU General Public License. “OpenNIUser Guide”, pp. 6–9 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Optics and PhotonicsBeijing Institute of TechnologyBeijingChina

Personalised recommendations