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Robust Background Subtraction for Quick Illumination Changes

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Advances in Image and Video Technology (PSIVT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4319))

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Abstract

This paper proposes a new method to extract moving objects from a color video sequence. The proposed method is robust to both noise and intensity changes in the observed image. A present background image is estimated by generating conversion tables from the original background image to the present image. Then, the moving object region is extracted by background subtraction. Using color gives more accurate detection than a previous method which used only monochrome data. Color images increase the computational load. The method addresses this problem by using the GPU’s throughput. Results are demonstrated with experiments on real data.

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References

  1. Sato, Y., Kaneko, S., Igarashi, S.: Robust Object Detection and Segmentation by Peripheral Increment Sign Correlation Image. Trans. of the IEICE J84-D-II(12), 2585–2594 (2001) (in Japanese)

    Google Scholar 

  2. Nagaya, S., Miyatake, T., Fujita, T., Ito, W., Ueda, H.: Moving Object Detection by Time-Correlation-Based Background Judgment Method. Transactions of the Institute of Electronics, Information and Communication Engineers D-II J79-D-II(4), 568–576 (1996) (in Japanese)

    Google Scholar 

  3. Habe, H., Wada, T., Matsuyama, T.: A Robust Background Subtraction Method under Varying Illumination. In: Tech. Rep. of IPSJ, SIG-CVIM115-3 (March 1999) (in Japanese)

    Google Scholar 

  4. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), Part vol. 2. IEEE Comput. Soc, Los Alamitos (1999)

    Google Scholar 

  5. Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis & Machine Intelligence 22(8), 747–757 (2000)

    Article  Google Scholar 

  6. KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proc. of the 2nd European Workshop on Advanced Video-Based Surveillance Systems (September 2001)

    Google Scholar 

  7. Fukui, S., Ishikawa, T., Iwahori, Y., Itoh, H.: Extraction of Moving Objects by Estimating Background Brightness. Trans. of IIEEJ 33(3), 350–357 (2004)

    Google Scholar 

  8. http://www.gpgpu.org/

  9. Griesser, A., De Roeck, S., Neubeck, A., Van Gool, L.: GPU-Based Foreground-Background Segmentation using an Extended Colinearity Criterion. In: Proc. of Vision, Modeling, and Visualization (VMV) (November 2005)

    Google Scholar 

  10. Matsui, R., Nagahara, H., Yachida, M.: An acceleration method of omnidirectional image processing using GPU. In: Proc. of Meeting on Image Recognition and Understanding (MIRU 2005), July 2005, pp. 1539–1546 (2005) (in Japanese)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Fukui, S., Iwahori, Y., Itoh, H., Kawanaka, H., Woodham, R.J. (2006). Robust Background Subtraction for Quick Illumination Changes. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_126

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  • DOI: https://doi.org/10.1007/11949534_126

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68297-4

  • Online ISBN: 978-3-540-68298-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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