A New Approach for Vehicle Detection in Congested Traffic Scenes Based on Strong Shadow Segmentation

  • Ehsan Adeli Mosabbeb
  • Maryam Sadeghi
  • Mahmoud Fathy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)


Intelligent traffic surveillance systems are assuming an increasingly important role in highway monitoring and city road management systems. Recently a novel feature was proposed to improve the accuracy of object localization and occlusion handling. It was constructed on the basis of the strong shadow under the vehicle in real-world traffic scene. In this paper, we use some statistical parameters of each frame to detect and segment these shadows. To demonstrate robustness and accuracy of our proposed approach, impressive results of our method in real traffic images including high congestion, noise, clutter, snow, and rain containing cast shadows, bad illumination conditions and occlusions, taken from both outdoor highways and city roads are presented.


Vehicle Detection Cast Shadow Shadow Detection Occlusion Handling Shadow Removal 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gutchess, D., Trajkovics, M., Cohen-Solal: A background model initialization algorithm for video surveillance. In: Proc. of IEEE ICCV 2001, Pt.1, pp. 744-740 (2001)Google Scholar
  2. 2.
    Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: Workshop on Motion and Video Comp. pp. 22–27 (2002)Google Scholar
  3. 3.
    Veeraraghavan, H., Masoud, O., Papanikolopoulos, N.: Computer vision algorithms for intersection monitoring. IEEE Trans. Intell.Transport. Syst. 4, 78–89 (2003)CrossRefGoogle Scholar
  4. 4.
    Jung, Y., Lee, K., Ho., Y.: Content-Based event retrieval using semantic scene interpretation for automated traffic surveillance. IEEE Transaction ITS 2, 151–163 (2001)Google Scholar
  5. 5.
    Memin, E., Perez, P.: Dense estimation and object-based segmentation of the optical flow with robust techniques. IEEE Trans. Image Process 7(5), 703–719 (1998)CrossRefGoogle Scholar
  6. 6.
    Chang, M., Tekalp, A., Sezan, M.: Simultaneous motion estimation and segmentation. IEEE Trans.Image Process 6, 1326–1333 (1997)CrossRefGoogle Scholar
  7. 7.
    Ristivojević, M., Konrad, J.: Joint space-time motion-based video segmentation and occlusion detection using multiphase level sets. In: IS&T/SPIE Symposium on Electronic Imaging, Visual Communications and Image Processing, San Jose, CA, USA, pp. 18–22 (2004)Google Scholar
  8. 8.
    Mitiche, A., El-Feghali, R., Mansouri, A.-R.: Tracking moving objects as spatio-temporal boundary detection. In: IEEE Southwest Symp. on Image Anal. Interp., pp. 110–206 (April 2002)Google Scholar
  9. 9.
    Nowak, E., Jurie, F.: Vehicle categorization: Parts for speed and accuracy. UJF – INPG, Societe Bertin - Technologies, Aix-en-Provence (2005)Google Scholar
  10. 10.
    Melo, J., Naftel, A., Bernardino, A., Santos-Victor, J.: Viewpoint independent detection of vehicle trajectories and lane geometry from uncalibrated Traffic Surveillance Cameras. In: ICIAR Conf. on Image Analysis and Recognition, Porto,Portugal, September 29-October 1 (2004)Google Scholar
  11. 11.
    Sadeghi, M., Fathy, M.: A Low-cost Occlusion Handling Using a Novel Featur. In: Congested Traffic Images. In: proceeding of IEEE ITSC 2006 Toronto pp. 522–527 (2006)Google Scholar
  12. 12.
    Huertas, A., Nevatia, R.: Detecting buildings in aerial images. Comput. Vis. Graph. Image Process 41, 31–152 (1988)CrossRefGoogle Scholar
  13. 13.
    Yoneyama, A., Yeh, C.H., Kuo, C.: Moving cast shadow elimination for robust vehicle extraction based on 2d joint vehicle/shadow models. In: IEEE Conf. on Advanced Video and Signal Based Surveillance, Miami, USA (July 2003)Google Scholar
  14. 14.
    Scanlan, J.M., Chabries, D.M., Christiansen, R.: A shadow detection and removal algorithm for 2-d images. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2057–2060 (1990)Google Scholar
  15. 15.
    Adjouadj, M.: Image analysis of shadows, depressions, and upright objects in the interpretation of real world scenes. IEEE Int. Conf. on Pattern Recog (ICPR), pp. 834–838 (1986)Google Scholar
  16. 16.
    Fung, G.S.K., Yung, N.H.C., Pang, G.K.H., Lai, A.H.S.: Effective moving cast shadows detection for monocular color image sequence. In: Proc. 11th ICIAP, pp. 404–409 (2001)Google Scholar
  17. 17.
    Nadimi, S., Bhanu, B.: Moving shadow detection using a physicsbased approach. In: Proc. IEEE Int. Conf. Pattern Recognition, vol. 2, pp. 701–704 (2002)Google Scholar
  18. 18.
    Gershon, R., Jepson, A., Tsotsos, J.: Ambient illumination and the determination of material changes. Journal of the Optical Society of America A 3(10), 1700–1707 (1986)CrossRefGoogle Scholar
  19. 19.
    Cavallaro, A., Salvador, E., Ebrahimi, T.: Shadow-aware object-based video processing. IEE Proc.-Vis. Image Signal Process 152(4), 398–406 (2005)CrossRefGoogle Scholar
  20. 20.
    Gevers, T., Stokman, H.: Classifying color edges in video into shadow-geometry, highlight, or material transitions. IEEE Trans. on Multimedia 5(2), 237–243 (2003)CrossRefGoogle Scholar
  21. 21.
    Forsyth, D., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, NY (2003)Google Scholar
  22. 22.
    Haritaoglu, I., Harwood, D., David, L.S.: W4 Real-time Surveillance of People and Their Activities. IEEE Trans. on Pattern Recog. and Machine Intelligence 22(8), 809–830 (2000)CrossRefGoogle Scholar
  23. 23.
    Wolf, C., Jolion, J.-M., Chassaing, F.: Text localization, enhancement and binarization in multimedia documents. In: Proc. of the ICPR 2002, vol. 2, pp. 1037–1040 (August 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ehsan Adeli Mosabbeb
    • 1
  • Maryam Sadeghi
    • 2
  • Mahmoud Fathy
    • 1
  1. 1.Computer Eng. Department, Iran University of Science and Technology, TehranIran
  2. 2.Computer Science Department, Simon Fraser University, VancouverCanada

Personalised recommendations