Skip to main content
SpringerLink
Log in

Background updating and shadow detection based on spatial, color, and texture information of detected objects

  • Regular Papers
  • Published:
Optical Review Aims and scope Submit manuscript

Abstract

Background model updating is a vital process for any background subtraction technique. This paper presents an updating mechanism that can be applied efficiently to any background subtraction technique. This updating mechanism exploits the color and spatial features to characterize each detected object. Spatial and color features are used to classify each detected object as a moving background object, a ghost, or a real moving object. The starting position of each detected object is the cue for updating background images. In addition, this paper presents a hybrid scheme to detect and remove cast shadows based on texture and color features. The robustness of the proposed method and its effectiveness in overcoming challenging problems such as gradual and sudden illumination changes, ghost appearance, non-stationary background objects, the stability of moving objects most of the time, and cast shadows are verified quantitatively and qualitatively.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Y. L. Tian and A. Hampapur: Proc. IEEE Workshop Motion and Video Computing (WACV/MOTION), 2005, p. 30.

  2. C. Braillon, C. Pradalier, J. L. Crowley, and C. Laugier: Proc. IEEE Symp. Intelligent Vehicles, 2006, p. 466.

  3. S. C. Cheung and C. Kamath: Proc. SPIE 5308 (2004) 881.

    Article  ADS  Google Scholar 

  4. C. Stauffer and W. Grimson: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR’99), 1999, p. 246.

  5. W. Grimson, C. Stauffer, R. Romano, and L. Lee: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR’98), 1998, p. 22.

  6. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers: Proc. 17th IEEE Int. Conf. Computer Vision (ICCV’99), 1999, p. 255.

  7. I. Haritaoglu, D. Harwood, and L. Davis: IEEE Trans. Pattern Anal. Mach. Intell. 22 (2000) 809.

    Article  Google Scholar 

  8. J. C. S. Jacques, Jr., C. R. Jung, and S. R. Musse: Proc. XVIII Brazilian Symp. Computer Graphics and Image Processing (SIBGRAPI’05), 2005, p. 189.

  9. A. Monnet, A. Mittal, N. Paragios, and V. Ramesh: Proc. 9th IEEE Int. Conf. Computer Vision (ICCV’ 03), 2003, p. 1305.

  10. A. Elgammal, D. Harwood, and L. S. Davis: Proc. European Conf. on Computer Vision (ECCV’00), 2000, p. 751.

  11. A. Tavakkoli, M. Nicolescu, G. Bebis, and M. Nicolescu: Int. J. Mach. Vision Appl. 20 (2009) 395.

    Article  Google Scholar 

  12. A. Hamad and N. Tsumura: Proc. 1st IEEE Asian Conf. Pattern Recognition (ACPR’11), 2011, p. 584.

  13. A. Hamad and N. Tsumura: in preparation for publication.

  14. R. S. Choras: Int. J. Biol. Biomed. Eng. 1 (2007) 6.

    Google Scholar 

  15. A. Joshi and N. Papanikolopoulos: IEEE Trans. Pattern Anal. Mach. Intell. 30 (2008) 2055.

    Article  Google Scholar 

  16. A. Prati, I. Mikic, M. M. Trivedi, and R. Cucchiara: IEEE Trans. Pattern Anal. Mach. Intell. 25 (2003) 918.

    Article  Google Scholar 

  17. N. Martel-Brisson and A. Zaccarin: IEEE Trans. Pattern Anal. Mach. Intell. 29 (2007) 1133.

    Article  Google Scholar 

  18. R. Cucchiara, C. Grana, M. Piccardi, A. Prati, and S. Sirotti: Proc. IEEE Conf. Intelligent Transportation Systems, 2001, p. 334.

  19. O. Schreer, I. Feldmann, U. Goelz, and P. Kauff: Proc. 4th IEEE Int. Symp. Video Processing and Multimedia Communication, 2002, p. 371.

  20. T. Horprasert, D. Hardwood, and L. S. Davis: Proc. Int. Conf. Computer Vision Frame-Rate Workshop, 1999, p. 1.

  21. A. Joshi and N. Papanikolopoulos: IEEE Int. Conf. Robotics and Automation (ICRA’08), 2008, p. 987.

  22. M. C. Padma and P. A. Vijaya: Int. J. Comput. Sci. Eng. 2 (2010) 115.

    Google Scholar 

  23. Z. Lin, Z. Jiang, and L. S. Davis: Proc. 12th IEEE Int. Conf. Computer Vision (ICCV’09), 2009, p. 444.

  24. C. Schuldt, I. Laptev, and B. Caputo: Proc. 17th Int. Conf. Pattern Recognition (ICPR’04), 2004, p. 32.

  25. L. Liyuan, H. Weimin, Y. Irene, and T. Qi: IEEE Trans. Image Process. 13 (2004) 1459.

    Article  ADS  Google Scholar 

  26. http://www.openvisor.org

  27. D. Xu, X. Li, Z. Liu, and Y. Yuan: Pattern Recognit. Lett. 26 (2005) 5.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Advanced Integration Science, Chiba University, Chiba, 263-8522, Japan

    Ahmed Mahmoud Hamad & Norimichi Tsumura

  2. Department of Information Technology, Faculty of Computers and Information, Menofia University, Cairo, Egypt

    Ahmed Mahmoud Hamad

Authors
  1. Ahmed Mahmoud Hamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Norimichi Tsumura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Mahmoud Hamad.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hamad, A.M., Tsumura, N. Background updating and shadow detection based on spatial, color, and texture information of detected objects. OPT REV 19, 182–197 (2012). https://doi.org/10.1007/s10043-012-0030-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10043-012-0030-x

Keywords

Search

Navigation