Advertisement

Efficient Moving Object Segmentation Algorithm for Illumination Change in Surveillance System

  • Tae-Yeon Jung
  • Ju-Young Kim
  • Duk-Gyoo Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3656)

Abstract

An efficient algorithm to segment the moving object is very important in the surveillance system. In general, the change detection by comparing brightness value is a good and simple method, but it shows a poor performance under illumination change. Therefore, we propose the segmentation algorithm to extract effectively the object in spite of the illumination change. There are three modes to extract the object, the criteria of mode selection are both available background existence and illumination change. Then the object is finally obtained by using projection and the morphological operator in post-processing. Furthermore, the double binary method using the similarity of brightness value and spatial proximity is used to obtain more edge information. A good segmentation performance is demonstrated by the simulation result.

Keywords

Current Frame Illumination Change Morphological Operation Initial Object Edge Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Wang, D.: Unsupervised video segmentation based on watershed and temporal tracking. IEEE Trans. Circuits Syst. Video Technol. 8, 539–546 (1998)CrossRefGoogle Scholar
  2. [2]
    Choi, J.C., Lee, S.-W., Mester, R.: Spatio-temporal video segmentation using a joint similarity measure. IEEE Trans. Circuits Syst. Video Technol. 7, 279–286 (1997)CrossRefGoogle Scholar
  3. [3]
    Neri, A., Colonnese, S., Russo, G., Talone, P.: Automatic moving object and background separation. Signal Processing 66(2), 219–232 (1998)zbMATHCrossRefGoogle Scholar
  4. [4]
    Guo, J., Kim, J.W., Kuo, C.-C.J.: Fast and accurate moving object extraction technique for MPEG-4 object-based video coding. In: SPIE, vol. 3653, pp. 1210–1221 (1999)Google Scholar
  5. [5]
    Kim, C.G., Hwang, J.N.: Fast and automatic video object segmentation and tracking for content-based applications. IEEE Trans. on Circuits and Systems for Video Technology 12(2), 122–129 (2002)CrossRefGoogle Scholar
  6. [6]
    Chien, S.Y., Ma, S.Y., Chen, L.G.: Efficient moving object segmentation algorithm using background registration technology. IEEE Trans. on Circuits and Systems for Video Technology 12(7), 577–586 (2002)CrossRefGoogle Scholar
  7. [7]
    Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. PAMI-6, 679–698 (1986)CrossRefGoogle Scholar
  8. [8]
    Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice-Hall, NJ (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tae-Yeon Jung
    • 1
  • Ju-Young Kim
    • 1
  • Duk-Gyoo Kim
    • 1
  1. 1.School of Electronic Engineering and Computer ScienceKyungpook National UniversityDaeguKorea

Personalised recommendations