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Learning Contextual Variations for Video Segmentation

  • Vincent Martin
  • Monique Thonnat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5008)

Abstract

This paper deals with video segmentation in vision systems. We focus on the maintenance of background models in long-term videos of changing environment which is still a real challenge in video surveillance. We propose an original weakly supervised method for learning contextual variations in videos. Our approach uses a clustering algorithm to automatically identify different contexts based on image content analysis. Then, state-of-the-art video segmentation algorithms (e.g. codebook, MoG) are trained on each cluster. The goal is to achieve a dynamic selection of background models. We have experimented our approach on a long video sequence (24 hours). The presented results show the segmentation improvement of our approach compared to codebook and MoG.

Keywords

video segmentation weakly supervised learning context awareness video surveillance cognitive vision 

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References

  1. 1.
    Prati, A., Mikic, I., Trivedi, M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(7), 918–923 (2003)CrossRefGoogle Scholar
  2. 2.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 246–252 (1999)Google Scholar
  3. 3.
    Elgammal, A.M., 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)CrossRefGoogle Scholar
  4. 4.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3), 172–185 (2005)CrossRefGoogle Scholar
  5. 5.
    Georis, B., Bremond, F., Thonnat, M.: Real-time control of video surveillance systems with program supervision techniques. Machine Vision and Applications 18(3-4), 189–205 (2007)zbMATHCrossRefGoogle Scholar
  6. 6.
    Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: ACM International Conference on Multimedia, pp. 65–73. ACM Press, New York, USA (1997)Google Scholar
  7. 7.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, pp. 226–231 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vincent Martin
    • 1
  • Monique Thonnat
    • 1
  1. 1.INRIA Sophia Antipolis, PULSAR project-teamSophia Antipolis

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