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Moving Object Detection Using Local Binary Pattern and Gaussian Background Model

  • A.P. AthiraEmail author
  • Midhula Vijayan
  • R. Mohan
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 11)

Abstract

It has been several years Background subtraction techniques were put into use in vision and image applications for motion detection. However, most of the methods fall short of providing fine results due to dynamic backgrounds, illumination variation, noise, etc. Uniqueness of the proposal is construction of a steady background from a video sequel. In the editorial, proposal is to develop a steady background representation from a certain video sequel. The background is updated on arrival of each frame. For detecting moving objects, the constructed background has been compared with diverse frames of the video sequel. For this, the background model is developed using combination of Local Binary Pattern (LBP) and Gaussian averaging. Gaussian averaging employs different forms that occur with time to confines the underlying opulence of the background. Likewise, a spatial region of hold is used by LBP. The projected proposal depends on spatio-temporal forms occurring with time to fabricate a suitable model background. Efficacy of the projected proposal is established by comparing the outcomes with some of the existing avant-garde background subtraction methods on open standard records.

Keywords

Gaussian model Local Binary Pattern Object detection 

References

  1. 1.
    Bovic, A.L.: Image and Video Processing. Academic Press, New York (2000)Google Scholar
  2. 2.
    Lin, W., Sun, M.T., Poovendran, R., Zhang, Z.: Activity recognition using a combination of category components and local models for video surveillance. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1128–1139 (2008)CrossRefGoogle Scholar
  3. 3.
    Piccardi, M., Jan, T.: Mean-shift background image modelling. In: Proceedings of International Conference on Image Processing, vol. 5, pp. 3399–3402 (2004)Google Scholar
  4. 4.
    Shi, Q., Cheng, L., Wang, L., Smola, A.: Discriminative human segmentation and recognition using semi-Markov model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  5. 5.
    Subudhi, B.N., Ghosh, S., Ghosh, A.: Moving object detection using Gaussian background model and Wronskian framework. In: Proceedings of 2nd International Conference on Advances in Computing, Communications and Informatics, pp. 1775–1780 (2013)Google Scholar
  6. 6.
    Heikkila, J., Silven, O.: A real-time system for monitoring of cyclists and pedestrians. In: Second IEEE Workshop on Visual Surveillance Fort Collins, Colorado, June 1999, pp. 74–81Google Scholar
  7. 7.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: realtime tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)CrossRefGoogle Scholar
  8. 8.
    Stauffer, C., Grimson, W.: Learning patterns of activity using real time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–767 (2000)CrossRefGoogle Scholar
  9. 9.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of International Conference on Computer Vision and Pattern recognition, pp. 2246–2252 (1999)Google Scholar
  10. 10.
    Chan, A., Mahadevan, V., Vasconcelos, N.: Generalized Stauffer and Grimson background subtraction for dynamic scenes. Mach. Vis. Appl. 22, 751–766 (2011)CrossRefGoogle Scholar
  11. 11.
    Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation invariant image description with local binary pattern histogram fourier features. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 61–70. Springer, Heidelberg (2009)Google Scholar
  14. 14.
    Zivkovic, Z.: Improved Adaptive Gaussian Mixture Model for Background Subtraction (2004)Google Scholar
  15. 15.
    KadewTraKuPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection (2001)Google Scholar
  16. 16.
    Zivkovic, Z.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of CSENIT TrichyTrichyIndia

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