Probabilistic Model-Based Background Subtraction

  • V. Krüger
  • J. Anderson
  • T. Prehn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


In this paper we introduce a model-based background subtraction approach where first silhouettes, which model the correlations between neightboring pixels are being learned and where then Bayesian propagation over time is used to select the proper silhouette model and tracking parameters. Bayes propagation is attractive in our application as it allows to deal with uncertainties in the video data during tracking. We eploy a particle filter for density estimation. We have extensively tested our approach on suitable outdoor video data.


Foreground Object Foreground Pixel Feature Extraction Technique Gait Recognition Tracking Parameter 
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.


  1. 1.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, SC, June 13-15, vol. 2, pp. 142–149 (2000)Google Scholar
  2. 2.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing 10, 197–209 (2000)CrossRefGoogle Scholar
  3. 3.
    Elgammal, A., Davis, L.: Probabilistic framework for segmenting people under occlusion. In: ICCV, ICCV 2001 (2001)Google Scholar
  4. 4.
    Gavrila, D., Philomin, V.: Real-time object detection for ”smart” vehicles. In: Proc. Int. Conf. on Computer Vision, Korfu, Greece, pp. 87–93 (1999)Google Scholar
  5. 5.
    Haritaoglu, I., Harwood, D., Davis, L.: W4s: A real-time system for detection and tracking people in 2.5 D. In: Proc. European Conf. on Computer Vision, Freiburg, Germany, June 1-5 (1998)Google Scholar
  6. 6.
    Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: Proceedings of IEEE ICCV 1999 FRAME-RATE Workshop (1999)Google Scholar
  7. 7.
    Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. Int. J. of Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar
  8. 8.
    Ivanov, Y.A., Bobick, A.F., Liu, J.: Fast lighting independent background subtraction. Int. J. of Computer Vision 37(2), 199–207 (2000)zbMATHCrossRefGoogle Scholar
  9. 9.
    Kale, A., Sundaresan, A., Rjagopalan, A.N., Cuntoor, N., Chowdhury, A.R., Krnger, V., Chellappa, R.: Identification of humans using gait. IEEE Trans. Image Processing 9, 1163–1173 (2004)CrossRefGoogle Scholar
  10. 10.
    Kitagawa, G.: Monta carlo filter and smoother for non-gaussian nonlinear state space models. J. Computational and Graphical Statistics 5, 1–25 (1996)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Krueger, V., Zhou, S.: Exemplar-based face recognition from video. In: Proc. European Conf. on Computer Vision, Copenhagen, Denmark, June 27-31 (2002)Google Scholar
  12. 12.
    Liu, J.S., Chen, R.: Sequential monte carlo for dynamic systems. Journal of the American Statistical Association 93, 1031–1041 (1998)Google Scholar
  13. 13.
    Toyama, K., Blake, A.: Probabilistic tracking in a metric space. In: Proc. Int. Conf. on Computer Vision, Vancouver, Canada, July 9-12, vol. 2, pp. 50–59 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • V. Krüger
    • 1
  • J. Anderson
    • 2
  • T. Prehn
    • 2
  1. 1.Aalborg Media LabAalborg University, CopenhagenBallerup
  2. 2.Aalborg University EsbjergEsbjergDenmark

Personalised recommendations