Advertisement

Deformable Part Model Based Hand Detection against Complex Backgrounds

  • Chunyu Zou
  • Yue LiuEmail author
  • Jiabin Wang
  • Huaqi Si
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 634)

Abstract

Hand detection is a challenging task in hand gesture recognition system and the detection results can be easily affected by changes in hand shapes, viewpoints, lightings or complex backgrounds. In order to detect and localize the human hands in static images against complex backgrounds, a hand detection method based on a mixture of multi-scale deformable part models is proposed in this paper, which is trained discriminatively using latent SVM and consists of three components each defined by a root filter and three part filters. The hands are detected in a feature pyramid in which the features are variants of HOG descriptors. The experimental results show that the proposed method is invariant to small deformations of hand gestures and the mixture model has a good performance on NUS hand gesture dataset - II.

Keywords

Hand detection Deformable part model Latent SVM HOG features Complex backgrounds 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China under grant No. 61370134, the National High Technology Research and Development Program of China (863 Program) under grant No. 2013AA013904.

References

  1. 1.
    Zhu, Y., Yang, Z., Yuan, B.: Vision based hand gesture recognition. In: 2013 International Conference on Service Sciences, pp. 260–265. IEEE Press, Shenzhen (2013)Google Scholar
  2. 2.
    Yu, C., Wang, X., Huang, H., Shen, J.: Vision-based hand gesture recognition using combinational features. In: 2010 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 543–546. IEEE Press, Darmstadt (2010)Google Scholar
  3. 3.
    Pisharady, P.K., Vadakkepat, P., Loh, A.P.: Attention based detection and recognition of hand postures against complex backgrounds. Int. J. Comput. Vis. 101, 403–419 (2013)CrossRefGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE Press, San Diego (2005)Google Scholar
  5. 5.
    Dadgostar, F., Sarrafzadeh, A., Messom, C.: Multi-layered hand and face tracking for real-time gesture recognition. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 587–594. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Dardas, N.H., Georganas, N.D.: Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Instrum. Meas. 60(11), 3592–3607 (2011)CrossRefGoogle Scholar
  7. 7.
    Dardas, N.H., Petriu, E.M.: Hand Gesture detection and recognition using principal component analysis. In: 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp. 1–6. IEEE Press, Ottawa (2011)Google Scholar
  8. 8.
    Viola, P., Jones, M.J.: Robust real-time object detection. Int. J. Comput. Vis. 2(57), 137–154 (2004)CrossRefGoogle Scholar
  9. 9.
    Stergiopoulou, E., Sgouropoulos, K., Nikolaou, N., Papamarkos, N.: Real time hand detection in a complex background. Eng. Appl. Artif. Intell. 35, 54–70 (2014)CrossRefGoogle Scholar
  10. 10.
    Fang, Y., Wang k., Cheng J., Lu, H., C.: A real-time hand gesture recognition method. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 995–998. IEEE Press, Beijing (2007)Google Scholar
  11. 11.
    Ong, E.J., Bowden, R.: A boosted classifier tree for hand shape detection. In: 6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 889–894. IEEE Press, Jeju Island (2006)Google Scholar
  12. 12.
    Wu, Y., Huang, T.S.: View-independent recognition of hand postures. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 88–94. IEEE Press, Hilton Head Island (2000)Google Scholar
  13. 13.
    Zondag, J.A., Gritti, T., Jeanne, V.: Practical study on real-time hand detection. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–8. IEEE Press, Amsterdam (2009)Google Scholar
  14. 14.
    Liew, C.F., Yairi, T.: Generalized BRIEF: a novel fast feature extraction method for robust hand detection. In: 2014 22nd International Conference on Pattern Recognition, pp. 3014–3019. IEEE Press, Stockholm (2014)Google Scholar
  15. 15.
    Mittal, A., Zisserman, A., Torr, P.H.S.: Hand detection using multiple proposals. In: 2011 British Machine Vision Conference, pp. 75.1–75.11. BMVA Press, Scotland (2011)Google Scholar
  16. 16.
  17. 17.
    Zhang, J.G., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)CrossRefGoogle Scholar
  18. 18.
    Felzenszwalb, P.F., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press, Anchorage (2008)Google Scholar
  19. 19.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  20. 20.
    Felzenszwalb, P.F., Huttenlocher D.: Distance transforms of sampled functions. Technical report 2004-1963, CIS, Cornell University (2004)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of OptoelectronicsBeijing Institute of TechnologyBeijingChina

Personalised recommendations