A Mixture of Gaussian-Based Method for Detecting Foreground Object in Video Surveillance

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 107)

Abstract

This chapter presents a novel MoG based method for foreground detection and segmentation in video surveillance. Normal MoG is difficult to deal with the foreground objects stay in the scene for a long time and segment different foreground objects from one blob. We improve MoG by adopting posterior feedback information of object tracking based on statistics to robustly modeling the background and to perfect the foreground segmentation result. Experiments and comparisons show that our method is robust and accurate for detecting foreground object in video surveillance.

Keywords

Video surveillance Mixture of Gaussian Posterior information Statistics Tracking 

Notes

Acknowledgments

This work is supported in part by Zhejiang natural science key foundation (Grant No.Z1101243 and No.Y1111017), national natural science foundation of China (Grant No.61003188), and Zhejiang Prior Themes and Social Development Project (Grand No.2009C03015-2).

References

  1. 1.
    Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. Comput Vis Pattern Recogn 2:246–252Google Scholar
  2. 2.
    Li L, Huang W, Gu IY, Tian Q (2002) Foreground object detection in changing background based on color co-occurrence statistics. IEEE Workshop on applications of computer vision, Orlando, 269–274Google Scholar
  3. 3.
    KaewTraKulPong P, Bowden R (2001) An improved adaptive background mixture model for real-time tracking with shadow detection. In: European workshop on advanced video based surveillance systems, Kluwer Academic, DordrechtGoogle Scholar
  4. 4.
    Rittscher J, Kato J, Joga S, Blake A (2000) A probabilistic background model for tracking. In: Proceedings of 6th European conference computer vision, vol 2, pp 336–350Google Scholar
  5. 5.
    Stenger B, Ramesh V, Paragios N, Coetzee F, Bouhman J (2001) Topology free hidden markov models: application to background modeling. In: Proceedings of IEEE international conference computer vision pp 294–301Google Scholar
  6. 6.
    Yang Y-H, Levine MD (1992) The background primal sketch: an approach for tracking moving objects. Mach Vis Appl 5:17–34CrossRefGoogle Scholar
  7. 7.
    Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. In: European conference computer vision 751–767Google Scholar
  8. 8.
    Askar H, Li X, Li Z (2002) Background clutter suppression and dim moving point targets detection using nonparametric method. In: International conference on communications, circuits and systems and West Sino expositions, vol 2, pp 982–986Google Scholar
  9. 9.
    Thirde D, Jones G (2004) Hierarchical probabilistic models for video object segmentation and tracking. In: International conference on pattern recognition, vol 1, pp 636–639Google Scholar
  10. 10.
    Oliver NM, Rosario B, Pentland AP (2000) A bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell 22:831–843CrossRefGoogle Scholar
  11. 11.
    Ko T, Soatto S, Estrin D (2008) Background subtraction on distributions. In: ECCV, vol 3, pp 276–289Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.College of Computer Science and Information EngineeringZhejiang Gongshang UniversityHangzhouChina

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