Efficient Real-Time Background Detection Based on the PCA Subspace Decomposition

  • Bogusław CyganekEmail author
  • Michał Woźniak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10245)


We investigate performance of the classical PCA based background subtraction procedure and compare it with the robust PCA versions which are computationally demanding. We show that the simple PCA based version endowed with the fast eigen-decomposition method allows real-time operation on VGA video streams while offering accuracy comparable with some of the robust versions.


Background subtraction Computer vision Real-time computations 



The authors would like to express their gratitude to prof. Ryszard Tadeusiewicz and prof. Janusz Kacprzyk for their great scientific influence and continuous support.

This work was supported by the Polish National Science Center NCN under the grant no. 2014/15/B/ST6/00609.

This work was also supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology.


  1. 1.
    Becker, S., Candes, E., Grant, M.: TFOCS: flexible first-order methods for rank minimization. In: Low-Rank Matrix Optimization Symposium, SIAM Conference on Optimization (2011)Google Scholar
  2. 2.
    Benezeth, Y., Jodoin, P-M., Emile, B., Laurent, H., Rosenberger, C.: Comparative study of background subtraction algorithms. SPIE J. Electron. Imaging 19(3), 033003 (2010)Google Scholar
  3. 3.
    Bingham, E., Hyvärinen, A.: A fast fixed-point algorithm for independent component analysis of complex valued signals. Int. J. Neural Syst. 10(1) (2000). World Scientic Publishing CompanyGoogle Scholar
  4. 4.
    Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: an overview. Comput. Sci. Rev. 11, 31–66 (2014)Google Scholar
  5. 5.
    Cyganek, B.: Object Detection and Recognition in Digital Images: Theory and Practice. Wiley, Hoboken (2013)Google Scholar
  6. 6.
    Cyganek, B., Gruszczyński, S.: Hybrid computer vision system for drivers’ eye recognition and fatigue monitoring. Neurocomputing 126, 78–94 (2014)CrossRefGoogle Scholar
  7. 7.
    Cyganek, B.: An analysis of the road signs classification based on the higher-order singular value decomposition of the deformable pattern tensors. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010. LNCS, vol. 6475, pp. 191–202. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17691-3_18 CrossRefGoogle Scholar
  8. 8.
    Demmel, J.W.: Applied Numerical Linear Algebra. Siam (1997)Google Scholar
  9. 9.
    Golub, G.H., van Loan, C.F.: Matrix Computations. Johns Hopkins Studies in the Mathematical Sciences. Johns Hopkins University Press (2013)Google Scholar
  10. 10.
    Guyon, C., Bouwmans, T., Zahzah, E.: Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis. Principal Component Analysis, Edited by Sanguansat, P. InTech (2012)Google Scholar
  11. 11.
  12. 12.
  13. 13.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real- time foreground-background segmentation using codebook model. Real-Time Imaging 11, 172–185 (2005)CrossRefGoogle Scholar
  14. 14.
    Kim, W., Kim, C.: Background subtraction for dynamic texture scenes using fuzzy color histograms. IEEE Signal Process. Lett. 3(19), 127–130 (2012)CrossRefGoogle Scholar
  15. 15.
    Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Marot, J., Fossati, C., Bourennane, S.: About advances in tensor data denoising methods. EURASIP J. Adv. Sig. Process. 2008, 12 (2008)Google Scholar
  17. 17.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)CrossRefGoogle Scholar
  18. 18.
    Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)Google Scholar
  19. 19.
    Papusha, I.: Fast Automatic Background Extraction Via Robust PCA. Stanford Electrical Engineering Department, Stanford (2011). Google Scholar
  20. 20.
    Stauffer C., Grimson W. E. L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE, NJ (1999)Google Scholar
  21. 21.
    Tadeusiewicz, R.: Introduction to intelligent systems. In: Wilamowski, B.M., Irvin, J.D. (eds.) The Industrial Electronics Handbook – Intelligent Systems, pp. 1–12. CRC Press, Boca Raton (2011)Google Scholar
  22. 22.
    Tadeusiewicz, R.: Neural networks in mining sciences – general overview and some representative examples. Archives of Mining Sciences (Archiwum Górnictwa), vol. 60, no. 4, pp. 971–984 (2015). ISSN 0860-7001Google Scholar
  23. 23.
    Torre, F.D.L., Black, M.: A framework for robust subspace learning. Int. J. Comput. Vis. 54, 117–142 (2003)Google Scholar
  24. 24.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Seventh International Conference on Computer Vision, Kerkyra, Greece, pp. 255–261, IEEE Computer Society Press, September 1999Google Scholar
  25. 25.
    Woźniak, M.: A hybrid decision tree training method using data streams. Knowl. Inf. Syst. 29(2), 335–347 (2011)CrossRefGoogle Scholar
  26. 26.
    Woźniak, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inform. Fusion 16(1), 3–17 (2014)CrossRefGoogle Scholar
  27. 27.
    Wright, J., Peng, Y., Ma, Y., Ganesh, A., Rao, S.: Robust principal component analysis: Exact recovery of corrupted low-rank matrices by convex optimization. Neural Information Processing Systems, NIPS (2009)Google Scholar
  28. 28.
  29. 29.
    Zadeh, L.A., Kacprzyk, J.: Fuzzy Logic for the Management of Uncertainty. John Wiley & Sons, Inc., New York (1992)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.Wroclaw University of Science and TechnologyWrocławPoland

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