Advertisement

Grading Tai Chi Performance in Competition with RGBD Sensors

  • Hui ZhangEmail author
  • Haipeng Guo
  • Chaoyun Liang
  • Ximin Yan
  • Jun Liu
  • Jie Weng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

In order to grade objectively, referees of Tai Chi practices always have to be very concentrated on every posture of the performer. This makes the referees easy to be fatigue and thus grade with occasional mistakes. In this paper, we propose using Kinect sensors to grade automatically. Firstly, we record the joint movement of the performer skeleton. Then we adopt the joint differences both temporally and spatially to model the joint dynamics and configuration. We apply Principal Component Analysis (PCA) to the joint differences in order to reduce redundancy and noise. We then employ non-parametric Nave-Bayes-Nearest-Neighbor (NBNN) as a classifier to recognize the multiple categories of Tai Chi forms. To give grade of each form, we study the grading criteria and convert them into decision on angles or distances between vectors. Experiments on several Tai Chi forms show the feasibility of our method.

Keywords

Tai Chi RGBD sensor Kinect 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bobick, A., Davis, J.: The recognition of human movement using temporal templates. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)CrossRefGoogle Scholar
  2. 2.
    Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  3. 3.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal featuresGoogle Scholar
  4. 4.
    Federation, I.W.: International wushu competition rules. International Wushu Federation (2005)Google Scholar
  5. 5.
    Han, J., Shao, L., X, D., Shotton, J.: Enhanced computer vision with microsoft kinect sensor: A review. IEEE Transactions on Cybernetics, 43(5) 1317–1333Google Scholar
  6. 6.
    Johansson, G.: Visual perception of biological motion and a model for its analysis. Journal of Attention Perception and Psychophysics 14(2), 201–211 (1973)CrossRefGoogle Scholar
  7. 7.
    Kaewplee, K., Khamsemanan, N., Nattee, C.: Muay thai posture classification using skeletal data from kinect and k-nearest neighbors. In: Proceedings of the International Conference on Information and Communication Technology for Embedded Systems (ICICTES 2014) (2014)Google Scholar
  8. 8.
    Klaser, A., Marszalek, M., Schmid, C.: A spatio-temporal descriptor based on 3d gradients. In: Proceedings of British Machine Vision Conference (2008)Google Scholar
  9. 9.
    Laptev, I.: On space-time interest points. International Journal of Computer Vision, 64(2)Google Scholar
  10. 10.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  11. 11.
    Lee, M.S., Ernst, E.: Systematic reviews of tai chi: An overview. British Journal of Sports Medicine 46(10), 713–718 (2011)CrossRefGoogle Scholar
  12. 12.
    Liu, L., Shao, L.: Learning discriminative representations from rgb-d video data. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)Google Scholar
  13. 13.
    Parameswaran, V., Chellappa, R.: View invariance for human action recognition. Journal of Attention Perception and Psychophysics 66(1), 83–101 (2001)Google Scholar
  14. 14.
    Sun, J., Wu, X., Yan, S., Cheong, L., Chua, T., Li, J.: Hierarchical spatio-temporal context modeling for action recognition. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 2004–2011 (2009)Google Scholar
  15. 15.
    Wang, J., Liu, Z., Wu, Y., Yuan, J.: Learning actionlet ensemble for 3d human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 914–927 (2014)CrossRefGoogle Scholar
  16. 16.
    Wu, D., Shao, L.: Leveraging hierarchical parametric networks for skeletal joints based action segmentation and recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA (2014)Google Scholar
  17. 17.
    Yang, X., Tian, Y.: Eigenjoints-based action recognition using nave-bayes-nearest-neighbor. In: Proc. Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 14–19 (2012)Google Scholar
  18. 18.
    Yuan, J., Liu, Z., Wu, Y.: Discriminative video pattern search for efficient action detection. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1728–1743 (2011)CrossRefGoogle Scholar
  19. 19.
    Zanfir, M., Leordeanu, M., Sminchisescu, C.: The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detectionGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hui Zhang
    • 1
    Email author
  • Haipeng Guo
    • 1
  • Chaoyun Liang
    • 1
  • Ximin Yan
    • 1
  • Jun Liu
    • 1
  • Jie Weng
    • 1
  1. 1.Department of Computer ScienceUnited International CollegeZhuhaiChina

Personalised recommendations