Background Updating for Visual Surveillance | SpringerLink
Skip to main content

Background Updating for Visual Surveillance

  • Conference paper
Advances in Visual Computing (ISVC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3804))

Included in the following conference series:

Abstract

Scene changes such as moved objects, parked vehicles, or opened/closed doors need to be carefully handled so that interesting foreground targets can be detected along with the short-term background layers created by those changes. A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model. In addition, important issues related to background updating for visual surveillance are discussed. Experimental results on surveillance examples, such as unloaded packages and unattended objects, are presented by showing those objects as short-term background layers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Int. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)

    Google Scholar 

  2. Harville, M.: A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 543–560. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  3. Javed, O., Shafique, K., Shah, M.: A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information. In: IEEE Workshop on Motion and Video Computing, MOTION 2002 (2002)

    Google Scholar 

  4. Porikli, F., Tuzel, O.: Human Body Tracking by Adaptive Background Models and Mean-Shift Analysis. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-ICVS (2003)

    Google Scholar 

  5. Elgammal, A., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  6. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: Int. Conf. Computer Vision, pp. 255–261 (1999)

    Google Scholar 

  7. Mittal, A., Paragios, N.: Motion-based Background Subtraction Using Adaptive Kernel Density Estimation. In: IEEE Conference in Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  8. Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background Modeling and Subtraction of Dynamic Scenes. In: IEEE International Conference on Computer Vision (ICCV), Nice, France (October 2003)

    Google Scholar 

  9. Stenger, B., Ramesh, V., Paragios, N., Coetzee, F., Buhmann, J.M.: Topology free hidden Markov models: application to background modeling. In: IEEE International Conference on Computer Vision, vol. 1, pp. 294–301 (2001)

    Google Scholar 

  10. Mittal, A., Davis, L.S.: M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 18–33. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  11. Paragios, N., Ramesh, V.: A MRF-based Real-Time Approach for Subway Monitoring. In: IEEE Conference in Computer Vision and Pattern Recognition (2001)

    Google Scholar 

  12. Zhong, J., Sclaroff, S.: Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter. In: IEEE International Conference on Computer Vision (2003)

    Google Scholar 

  13. Garg, K., Nayar, S.K.: Detection and Removal of Rain from Videos. In: IEEE Computer Vision and Pattern Recognition (CVPR), Washington (July 2004)

    Google Scholar 

  14. Zhao, T., Nevatia, R.: Tracking Multiple Humans in Crowded Environment. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2004)

    Google Scholar 

  15. Ren, Y., Chua, C., Ho, Y.: Statistical background modeling for non-stationary camera. Pattern Recognition Letters 24(1-3), 183–196 (2003)

    Article  MATH  Google Scholar 

  16. Hayman, E., Eklundh, J.-O.: Statistical Background Subtraction for a Mobile Observer. In: IEEE International Conference on Computer Vision (2003)

    Google Scholar 

  17. Walther, D., Edgington, D.R., Koch, C.: Detection and Tracking of Objects in Underwater Video. In: IEEE International Conference on Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  18. Matsushita, Y., Nishino, K., Ikeuchi, K., Sakauchi, M.: Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1336–1347 (2004)

    Article  Google Scholar 

  19. Davis, J.W., Sharma, V.: Robust Background-Subtraction for Person Detection in Thermal Imagery. In: Joint IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum (2004)

    Google Scholar 

  20. Yalcin, H., Black, M.J., Fablet, R.: The Dense Estimation of Motion and Appearance in Layers. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2004)

    Google Scholar 

  21. Ke, Q., Kanade, T.: A Robust Subspace Approach to Layer Extraction. In: IEEE Workshop on Motion and Video Computing (Motion 2002), pp. 37–43 (2002)

    Google Scholar 

  22. Torr, P.H.S., Szeliski, R., Anandan, P.: An Integrated Bayesian Approach to Layer Extraction from Image Sequences. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 297–303 (2001)

    Article  Google Scholar 

  23. Smith, P., Drummond, T., Cipolla, R.: Layered Motion Segmentation and Depth Ordering by Tracking Edges. IEEE Trans. Pattern Anal. Mach. Intell. (April 2004)

    Google Scholar 

  24. Frey, B.J., Jojic, N., Kannan, A.: Learning Appearance and Transparency Manifolds of Occluded Objects in Layers. In: IEEE International Conference on Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  25. Schodl, A., Essa, I.A.: Depth layers from occlusions. In: IEEE International Conference on Computer Vision and Pattern Recognition (2001)

    Google Scholar 

  26. Zhou, Y., Tao, H.: Background Layer Model for Object Tracking through Occlusion. In: IEEE International Conf. on Computer Vision, ICCV 2003, pp. 1079–1085 (2003)

    Google Scholar 

  27. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background Modeling and Subtraction by Codebook Construction. In: IEEE International Conference on Image Processing, ICIP (2004)

    Google Scholar 

  28. Harwood, D., Subbarao, M., Hakalahti, H., Davis, L.S.: A New Class of Edge-Preserving Smoothing Filters. Pattern Recognition Letters 6, 155–162 (1987)

    Article  Google Scholar 

  29. Haritaoglu, I., Harwood, D., Davis, L.S.: W 4: real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)

    Article  Google Scholar 

  30. Cheng, H.-D., Jiang, X.-H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34(12) (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, K., Harwood, D., Davis, L.S. (2005). Background Updating for Visual Surveillance. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_41

Download citation

  • DOI: https://doi.org/10.1007/11595755_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30750-1

  • Online ISBN: 978-3-540-32284-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics