Evolutionary Algorithm-Based Background Generation for Robust Object Detection

  • Taekyung Kim
  • Seongwon Lee
  • Joonki Paik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


One of the most fundamental image analysis models is background generation that helps to extract information and features in still images and sequential images. Since conventional approaches generate the background from intensity values of the image affected by illumination, the resulting background is often unsatisfactory. In case of background generation with sequential images, noises and the changes of illumination causes errors in the generated background. In this paper we propose an efficient background generation algorithm based on generic algorithm. The proposed algorithm calculates the suitability of changing regions of sequential images, and then causes evolution to the next generation to obtain a clear background. In the proposed evolutionary algorithm, the chromosome includes edges and intensity values of the images so that the algorithm can effectively exclude incorrect information caused by the change of illumination and generates an image of pure background.


Input Image Median Filter Background Generation Illumination Change Frame Difference 
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  1. 1.
    Koo, J.H., et al.: Development of FPGA-based Adaptive Image Enhancement Filter System using Genetic Algorithm. In: Proc. Congress on Evolutionary Computation, vol. 2, pp. 1480–1485 (2002)Google Scholar
  2. 2.
    Naohiro, A., Akihiro, F.: Detecting Obstructions and Tracking Moving Objects by Image Processing Technique. Electronics and Communications in Japan 2 (1999)Google Scholar
  3. 3.
    Wixson, L.: Illumination Assessment for Vision-based Real-time Traffic Monitoring. In: Proc. Int’l Conf. Pattern Recognition, pp. 56–62 (1996)Google Scholar
  4. 4.
    Haritaoglu: W4: Real-Time Surveillance of People and Their Activities. IEEE Trans. 22 (2000)Google Scholar
  5. 5.
    Yasuyuki, M., Ko, N., Katsushi, I., Msao, S.: Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance. IEEE Trans. Pattern Analysis and Machine Intelligence 26 (2004)Google Scholar
  6. 6.
    Chien, S.Y., Ma, S.Y., Chen, L.G.: Efficient Moving Object Segmentation Algorithm Using Background Registration Technique. IEEE Trans. CIRCUITS and SYSTEMS for Video technology 12 (2002)Google Scholar
  7. 7.
    Long, W., Yang, Y.H.: Stationary Background Generation: An alternative to the Difference of two Images. Pattern Recognition 23, 1351–1359 (1990)CrossRefGoogle Scholar
  8. 8.
    Noever, D., Baskaran, S.: Steady-state vs. Generational Genetic Algorithms: A Comparison of Time Complexity and Convergence Properties. Santa Fe Institute Working Papers, pp. 1–33 (1992)Google Scholar
  9. 9.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Taekyung Kim
    • 1
  • Seongwon Lee
    • 2
  • Joonki Paik
    • 1
  1. 1.Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and FilmChung-Ang UniversitySeoulKorea
  2. 2.Department of Computer Engineering, College of Electronics and InformationKwangwoon UniversitySeoulKorea

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