Examining Quality of Hand Segmentation Based on Gaussian Mixture Models

  • Michal Lech
  • Piotr Dalka
  • Grzegorz Szwoch
  • Andrzej Czyżewski
Part of the Communications in Computer and Information Science book series (CCIS, volume 429)


Results of examination of various implementations of Gaussian mixture models are presented in the paper. Two of the implementations belonged to the Intel’s OpenCV 2.4.3 library and utilized Background Subtractor MOG and Background Subtractor MOG2 classes. The third implementation presented in the paper was created by the authors and extended Background Subtractor MOG2 with the possibility of operating on the scaled version of the original video frame and additional image post-processing phase. The algorithms have been evaluated for various conditions related to stability of background. The quality of hand segmentation when a whole user’s body is visible in the video frame and when only a hand is present has been assessed. Three measures, based on false negative and false positive errors, were calculated for the assessment of segmentation quality, i.e. precision, recall and accuracy factors.


Gaussian mixture models hand segmentation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Van den Bergh, M., Van Gool, L.: Combining RGB and ToF cameras for real-time 3D hand gesture interaction. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 66–72 (2011)Google Scholar
  2. 2.
    Yin, X., Guo, D., Xie, M.: Hand image segmentation using color and RCE neural network. Robotics and Autonomous Systems 34(4), 235–250 (2001)CrossRefMATHGoogle Scholar
  3. 3.
    Lech, M., Kostek, B., Czyżewski, A.: Virtual Whiteboard: A gesture-controlled pen-free tool emulating school whiteboard. Intelligent Decision Technologies, Multimedia/Multimodal Human-Computer Interaction in Knowledge-based Environments 6(2), 161–169 (2012)Google Scholar
  4. 4.
    Lech, M., Kostek, B.: Testing the novel gesture-based mixing interface. Journal of the Audio Engineering Society 61(5), 301–313 (2013)Google Scholar
  5. 5.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly, Sebastopol (2008)Google Scholar
  6. 6.
    Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: 13th Conf. on Uncertainty in Artificial Intelligence, pp. 175–181 (1997)Google Scholar
  7. 7.
    Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Trans. on Pattern Analysis and Machine Intell. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  8. 8.
    Laganiere, R.: OpenCV 2 Computer Vision Application Programming Cookbook: Over 50 recipes to master this library of programming functions for real-time computer vision. Packt Publishing (2011)Google Scholar
  9. 9.
    Suo, P., Wang, Y.: An Improved Adaptive Background Modelling Algorithm Based on Gaussian Mixture Model. In: ICSP, pp. 1436–1439 (2008)Google Scholar
  10. 10.
    Chen, G., Yu, Z., Wen, Q., Yu, Y.: Improved Gaussian Mixture Model for Moving Object Detection. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds.) AICI 2011, Part I. LNCS, vol. 7002, pp. 179–186. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Dong, L.: Moving Objects Detection Method Based on a Fast Convergence Gaussian Mixture Model. In: 3rd International Conference on Computer Research and Development, China, pp. 269–273 (2011)Google Scholar
  12. 12.
    Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: 2nd European Workshop on Advanced Video Based Surveillance Systems (2001)Google Scholar
  13. 13.
    Zivkovic, Z.: Improved adaptive Gausian mixture model for background subtraction. In: International Conference Pattern Recognition, UK, vol. 2, pp. 28–31 (August 2004)Google Scholar
  14. 14.
    Dalka, P.: Multi-camera Vehicle Tracking Using Local Image Features and Neural Networks. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2012. CCIS, vol. 287, pp. 58–67. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michal Lech
    • 1
  • Piotr Dalka
    • 1
  • Grzegorz Szwoch
    • 1
  • Andrzej Czyżewski
    • 1
  1. 1.Multimedia Systems DepartmentGdansk Univ. of Technology, Faculty of Electronics, Telecommunications and InformaticsGdanskPoland

Personalised recommendations