Foreground-to-Ghost Discrimination in Single-Difference Pre-processing

  • Francesco Archetti
  • Cristina E. Manfredotti
  • Vincenzina Messina
  • Domenico G. Sorrenti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


It is well known that motion detection using single frame differencing, while computationally much simpler than other techniques, is more liable to generate large areas of false foregrounds known as ghosts. In order to overcome this problem the authors propose a method based on signed differencing and connectivity analysis. The proposal is suitable to applications which cannot afford the un-avoidable errors of background modeling or the limitations of 3-frames preprocessing.


Background Modeling Current Image Background Image Connectivity Analysis Foreground Pixel 
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.
    Amamoto, N., Fujii, A.: Detecting obstructions and tracking moving objects by image processing techniques. Electronics and Comm. Japan, Part 3 82, 28–37 (1999)CrossRefGoogle Scholar
  2. 2.
    Gloyer, B., Aghajan, H.K., Kailath, T.: Video-based freeway monitorig system using recursive vehicle tracking. In: Proceedings of SPIE, pp. 173–180 (1995)Google Scholar
  3. 3.
    McKenna, S., Jabri, S., Duric, Z., Wechsler, H.: Tracking interacting people. In: 4th Int. Conf. on Automatic Face and Gesture Recognition, Grenoble, France, pp. 384–353 (2000)Google Scholar
  4. 4.
    Cheung, S.C., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Video Communications and Image Processing, SPIE Electronic Imaging, San Jose (2004)Google Scholar
  5. 5.
    Cheung, S.C., Kamath, C.: Robust background subtraction with foreground validation for urban traffic video. EURASIP Journal on Applied Signal Processing 14, 1–11 (2005)Google Scholar
  6. 6.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.S.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11, 172–185 (2005)CrossRefGoogle Scholar
  7. 7.
    Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modelling using non-parametric kernel density estimation for visual survillance. Proc. of IEEE (2002)Google Scholar
  8. 8.
    Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: Proc. Thirteenth Conf. on Uncertainty in Artificial Intelligence (UAI 1997) (1997)Google Scholar
  9. 9.
    Stauffer, C., Grimson, W.: Learning patterns of activity using real time tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 747–757 (2000)CrossRefGoogle Scholar
  10. 10.
    Yoshinari, K., Michihito, M.: A human motion estimation method using 3-successive video frames. In: Proc. of Int. Conf. on Virtual Systems and Multimedia (GIFU), pp. 135–140 (1996)Google Scholar
  11. 11.
    Zhang, C., Chen, S., Shyu, M., Peeta, S.: Adaptive background learning for vehicle detection and spatio-temporal tracking. In: Information, Communications and Signal Processing (2003)Google Scholar
  12. 12.
    Cutler, R., Davis, L.: View-based detection. In: Proceedings Fourteenth International Conference on Pattern Recognition, Brisbone, Australia, pp. 495–500 (1998)Google Scholar
  13. 13.
    Cucchiara, R., Piccardi, M., Prati, A.: Detecting moving objects, ghost, and shadows in video streams. IEEE transactions on Pattern Analysis and Machine Intelligence, 1337–1342 (2003)Google Scholar
  14. 14.
    Zhou, Q., Aggorwal, J.: Tracking and classifying moving objects from videos. In: Proceedings of IEEE Workshop on Performance Evaluation of Tracking and Survillance (2001)Google Scholar
  15. 15.
    Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Publishing Company, Reading (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Francesco Archetti
    • 1
    • 2
  • Cristina E. Manfredotti
    • 2
  • Vincenzina Messina
    • 2
  • Domenico G. Sorrenti
    • 2
  1. 1.Consorzio Milano RicercheMilanItaly
  2. 2.Università Milano-BicoccaMilanItaly

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