Advertisement

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

  • Sébastien PiérardEmail author
  • Marc Van Droogenbroeck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

The quest for the “best” background subtraction technique is ongoing. Despite that a considerable effort has been undertaken to develop flexible and efficient methods, some elementary questions are still unanswered. One of them is the existence of an intrinsic upper bound to the performance. In fact, data are affected by noise, and therefore it is illusory to believe that it is possible to achieve a perfect segmentation. This paper aims at exploring some intrinsic limitations of the principle of background subtraction. The purpose consists in studying the impact of several limiting factors separately. One of our conclusions is that even if an algorithm would be able to calculate a perfect background image, it is not sufficient to achieve a perfect segmentation with background subtraction, due to other intrinsic limitations.

Keywords

Video Sequence Input Image Background Subtraction Local Binary Pattern Video Surveillance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Barnich, O., Van Droogenbroeck, M.: ViBe: A universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: An overview. Computer Science Review 11–12, 31–66 (2014)CrossRefGoogle Scholar
  3. 3.
    Brutzer, S., Höferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: IEEE Int. Conf. Comput. Vision and Pattern Recognition (CVPR), Providence, Rhode Island, USA, pp. 1937–1944, June 2011Google Scholar
  4. 4.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  5. 5.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: IEEE Int. Conf. Comput. Vision and Pattern Recognition Workshop (CVPRW), Providence, Rhode Island, USA, June 2012Google Scholar
  7. 7.
    Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: A novel video dataset for change detection benchmarking. IEEE Trans. Image Process. 23(11), 4663–4679 (2014)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Gruenwedel, S., Van Hese, P., Philips, W.: An edge-based approach for robust foreground detection. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 554–565. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  9. 9.
    Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)CrossRefGoogle Scholar
  10. 10.
    Jodoin, P.-M., Piérard, S., Wang, Y., Van Droogenbroeck, M.: Overview and benchmarking of motion detection methods. In: Bouwmans, T., Porikli, F., Hoferlin, B., Vacavant, A. (eds.) Background Modeling and Foreground Detection for Video Surveillance, chapter 24. Chapman and Hall/CRC, July 2014Google Scholar
  11. 11.
    Maddalena, L., Bouwmans, T.: Scene background modeling and initialization (SBMI) workshop, September 2015. http://sbmi2015.na.icar.cnr.it
  12. 12.
    Parks, D., Fels, S.: Evaluation of background subtraction algorithms with post-processing. In: IEEE Int. Conf. Advanced Video and Signal Based Surveillance, Santa Fe, New Mexico, USA, pp. 192–199, September 2008Google Scholar
  13. 13.
    St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: SuBSENSE: A universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Stauffer, C., Grimson, E.: Adaptive background mixture models for real-time tracking. In: IEEE Int. Conf. Comput. Vision and Pattern Recognition (CVPR), Ft. Collins, USA, vol. 2, pp. 246–252, June 1999Google Scholar
  15. 15.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)CrossRefGoogle Scholar
  16. 16.
    Zivkovic, Z.: Improved adaptive gausian mixture model for background subtraction. In: IEEE Int. Conf. Pattern Recognition (ICPR), , Cambridge, UK, vol. 2, pp. 28–31, August 2004Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.INTELSIG Laboratory, Department of Electrical Engineering and Computer ScienceUniversity of LiègeLiègeBelgium

Personalised recommendations