Advertisement

Adaptive Background Modeling for Paused Object Regions

  • Atsushi Shimad
  • Satoshi Yoshinaga
  • Rin-ichiro Taniguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

Background modeling has been widely researched to detect moving objects from image sequences. Most approaches have a falsenegative problem caused by a stopped object. When a moving object stops in an observing scene, it will be gradually trained as background since the observed pixel value is directly used for updating the background model. In this paper, we propose 1) a method to inhibit background training, and 2) a method to update an original background region occluded by stopped object. We have used probabilistic approach and predictive approach of background model to solve these problems. The great contribution of this paper is that we can keep paused objects from being trained.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. Computer Vision and Pattern Recognition 2, 246–252 (1999)Google Scholar
  2. 2.
    Shimada, A., Arita, D., Taniguchi, R.: Dynamic Control of Adaptive Mixture-of-Gaussians Background Model. In: CD-ROM Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance 2006 (2006)Google Scholar
  3. 3.
    Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and Foreground Modeling Using Non-parametric Kernel Density Estimation for Visual Surveillance. Proceedings of the IEEE 90, 1151–1163 (2002)CrossRefGoogle Scholar
  4. 4.
    Tanaka, T., Shimada, A., Arita, D., Taniguchi, R.: A Fast Algorithm for Adaptive Background Model Construction Using Parzen Density Estimation. In: CD-ROM Proc. of IEEE International Conference on Advanced Video and Signal based Surveillance (2007)Google Scholar
  5. 5.
    Shimada, A., Taniguchi, R.: Hybrid Background Model using Spatial-Temporal LBP. In: IEEE International Conference on Advanced Video and Signal based Surveillance 2009 (2009)Google Scholar
  6. 6.
    Satoh, Y., Shun’ichi Kaneko, N.Y., Yamamoto, K.: Robust object detection using a Radial Reach Filter(RRF). Systems and Computers in Japan 35, 63–73 (2004)CrossRefGoogle Scholar
  7. 7.
    Tanaka, T., Shimada, A., Taniguchi, R., Yamashita, T., Arita, D.: Towards robust object detection: integrated background modeling based on spatio-temporal features. In: Asian Conference on Computer Vision 2009 (2009)Google Scholar
  8. 8.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principle and Practice of Background Maintenance. In: International Conference on Computer Vision, pp. 255–261 (1999)Google Scholar
  9. 9.
    Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. Computer Vision and Pattern Recognition, 1–8 (2008)Google Scholar
  10. 10.
    Porikli, F., Ivanov, Y., Haga., T.: Robust abandoned object detection using dual foreground. EURASIP Journal on Advances in Signal Processing (2008)Google Scholar
  11. 11.
    li Tian, Y., Feris, R., Hampapur, A.: Real-time detection of abandoned and removed objects in complex environments. In: International Workshop on Visual Surveillance - VS 2008 (2008)Google Scholar
  12. 12.
    Holt Charles, C.: Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting 20, 5–10 (2004)CrossRefGoogle Scholar
  13. 13.
    Heikkilä, M., Pietikäinen, M., Heikkilä, J.: A texture based method for detecting moving objects. In: British Machine Vision Conf., vol. 1, pp. 187–196 (2004)Google Scholar
  14. 14.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)CrossRefzbMATHGoogle Scholar
  15. 15.
    Martel-Brisson, N., Zaccarin, A.: Moving cast shadow detection from a gaussian mixture shadow model. Computer Vision and Pattern Recognition, 643–648 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Atsushi Shimad
    • 1
  • Satoshi Yoshinaga
    • 1
  • Rin-ichiro Taniguchi
    • 1
  1. 1.Kyushu UniversityFukuokaJapan

Personalised recommendations