Adaptive Background Generation for Video Object Segmentation

  • Taekyung Kim
  • Joonki Paik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


In this paper, we present a novel method for generating background that adopts frame difference and a median filter to sensitive areas where illumination changes occur. The proposed method also uses fewer frames than the existing methods. Background generation is widely used as a preprocessing for video-based tracking, surveillance, and object detection. The proposed background generation method utilizes differences and motion changes between two consecutive frames to cope with the changes of illumination in an image sequence. It also utilizes a median filter to adaptively generate a robust background. The proposed method enables more efficient background reconstruction with fewer frames than existing methods use.


Median Filter Background Image Background Generation Illumination Change Object Segmentation 
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.


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  1. 1.
    Long, W., Yang, Y.H.: Stationary background generation: An alternative to the difference of two images. Pattern Recognition 23(12), 1351–1359 (1990)CrossRefGoogle Scholar
  2. 2.
    Naohiro, A., Akihiro, F.: Detecting Obstructions and Tracking Moving Objects by Image Processing Technique. Electronics and Communications in Japan, Part. 3. 2(11) (1999)Google Scholar
  3. 3.
    Wixson, L.: Illumination assessment for Vision-based real-time traffic monitoring. In: Proc. Int. Conf. Pattern Recognition, pp. 56–62 (1996)Google Scholar
  4. 4.
    Fathy, M., Siyal, M.Y.: A window-based edge detection technique for measuring road traffic parameters in real-time. Real-Time Imaging 1, 297–305 (1995)CrossRefGoogle Scholar
  5. 5.
    Lee, B., Hedley, M.: Background Estimation for Video surveillance. In: Int. Image Processing and Computer Vision (ICIAP 2001) (April 2005)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(7) (July 2002)Google Scholar
  7. 7.
    Haritaoglu, I.: W4:real-time Surveillance of people and their activates. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8) (2000)Google Scholar
  8. 8.
    Matsushita, Y., Nishino, K., Ikeuchi, K., Sakauchi, M.: Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance. IEEE Trans. Pattern Analysis and Machine Intelligence 26(10) (October 2004)Google Scholar
  9. 9.
    Cucchiara, R., Costantino, G., Massimo, P., Andrea, P.: Detecting Moving Objects, Ghosts, and Shadows in Video Streams. IEEE Trans. Pattern Analysis and Machine Intelligence 25(10) (October 2003)Google Scholar
  10. 10.
    Li, L., Huang, W., Gu, I.H., Tian, Q.: Forground Object Detection in Changing Background Based on Color Co-Occurrence Statistics. In: Proc. IEEE Workshop on Application of Computer Vision (WACV 2002), pp. 269–274 (December 2002)Google Scholar
  11. 11.
    Fang, Y., Masaki, I., Herthold, B.: Distance/Motion-based Segmentation under Heavy Background Noise. In: IEEE Intelligent Vehicles Symposium (IV2002), pp. 483–488 (July 2002)Google Scholar
  12. 12.
    Ren, Y., Chua, C.S., Ho, Y.K.: Statistical background modeling for non-stationary camera. Pattern Recognition Letter 24, 183–196 (2003)MATHCrossRefGoogle Scholar
  13. 13.
    Kim, S.J., Shin, S.H., Paik, J.K.: Real-time iterative framework of regularized image restoration and its application to video enhancement. Real-Time Imaging 10, 37–50 (2003)MATHGoogle Scholar
  14. 14.
    Koschan, A., Kang, S., Paik, J.K., Abidi, B.R., Abidi, M.A.: Color active shape models for tracking non-rigid objects. Pattern Recognition Letters 24, 1751–1765 (2003)CrossRefGoogle Scholar
  15. 15.
    Sun, Y., Paik, J.K., Koschan, A., Page, D.L., Abidi, M.: Point fingerprint: A new 3-D object representation scheme. IEEE Trans. Systems, Man and Cybernetics, Part B 33, 712–717 (2003)CrossRefGoogle Scholar
  16. 16.
    Kim, Y., Yoo, J., Lee, S., Shin, J., Paik, J.K., Jung, H.: Adaptive mode decision for H.264 encoder. Electronics Letters 40, 1172–1173 (2004)CrossRefGoogle Scholar
  17. 17.
    Kong, S., Heo, J., Abidi, B., Paik, J.K., Abidi, M.: Recent advances in visual and infrared face recognition – A review. Computer Vision and Image Understanding 97, 103–135 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Taekyung Kim
    • 1
  • 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

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