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Dynamic Background Discrimination with a Recurrent Network

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Book cover Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

Discrimination between the moving foreground objects and the complex dynamic background is a challenging task. In this paper, we have proposed a probabilistic graphical model – a recurrent stochastic network, which is able to learn the temporal and the spatial correlation from the video input data and make inference with a generalized belief propagation algorithm. Experiments have shown that the proposed recurrent network can model the dynamic backgrounds containing swaying trees, bushes and moving ocean waves. Very promising segmentation results have been obtained.

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References

  1. Aggarwal, J.K., Cai, Q.: Human Motion Analysis: A Review. Computer Vision and Image Understanding 73(3), 428–440 (1999)

    Article  Google Scholar 

  2. Dougherty, E.R.: Random Process for Image and Signal Processing. IEEE Press, New York (1999)

    Google Scholar 

  3. Fan, J., Dimitrova, N.: Online Face Recognition System For Videos Based On Modified Probabilistic Neural Networks. In: International Conference on Image Processing (2004)

    Google Scholar 

  4. Feng, X., Williams, C., Felderhof, S.: Combining Belief Networks and Neural Networks for Scene Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 467–482 (2002)

    Article  Google Scholar 

  5. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)

    Article  MATH  Google Scholar 

  6. Hammersley, J.M., Clifford, P.: Markov field on finite graphs and lattices (1971) (unpublished)

    Google Scholar 

  7. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)

    Article  Google Scholar 

  8. Huang, R., Pavlovic, V., Metaxas, D.N.: A Graphical Model Framework for Coupling MRFs and Deformable Models. In: International Conference on Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  9. Ivanovic, A., Huang, T.S.: A Probabilistic Framework for Segmentation and Tracking of Multiple non Rigid Objects for Video Surveillance. In: International Conference on Image Processing (2004)

    Google Scholar 

  10. Jordan, M.: Learning in Graphical Models. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  11. Lee, T.S., Mumford, D.: Hierarchical Bayesian inference in the visual cortex. Journal of Optical Society of America, A. 20(7), 1434–1448 (2003)

    Article  Google Scholar 

  12. Li, L., Huang, W.M., Gu, I.Y.H., Tian, Q.: Foreground object detection from videos containing complex background. In: Proc. of ACM Multimedia Conf., USA (2003)

    Google Scholar 

  13. Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Tokyo (2001)

    MATH  Google Scholar 

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

    Google Scholar 

  15. Rolls, E.T., Deco, G.: Computational Neuroscience of Vision. Oxford University Press, New York (2002)

    Google Scholar 

  16. Seki, M., Wada, T., Fujiwara, H., Sumi, K.: Background Subtraction based on Co-occurrence of Image Variations. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  17. Tino, P., Cernansky, M., Benuskova, L.: Markovian Architectural Bias of Recurrent Neural Networks. IEEE Transactions on Neural Networks 15(1) (2004)

    Google Scholar 

  18. Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding Belief Propagation and its Generalizations. Mitsubishi Electric Research Laboratories, Technical Report (2001)

    Google Scholar 

  19. Zhao, J.: A Recurrent Stochastic Binary Network. Science in China, Ser. F 44(5) (2001)

    Google Scholar 

  20. Zhu, S.C.: Statistical Modeling and Conceptualization of Visual Patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(6), 691–712 (2003)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. The Research Institute of Computer Science and Technology, Ningbo University, Ningbo, 315211, China

    Jieyu Zhao

Authors
  1. Jieyu Zhao
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhao, J. (2005). Dynamic Background Discrimination with a Recurrent Network. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_63

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  • DOI: https://doi.org/10.1007/11539117_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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