Cast Shadow Detection Based on Semi-supervised Learning

  • Salma Kammoun Jarraya
  • Rania Rebai Boukhriss
  • Mohamed Hammami
  • Hanene Ben-Abdallah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7324)


In this paper, we tackle the shadow problem in depth for a better foreground segmentation. We propose a novel variant of co-training technique for shadow detection and removal in uncontrolled scenes. This variant works according to a powerful temporal behavior. Setting co-training parameters is based on an extensive experimental study. The proposed co-training variant runs periodically to obtain more generic classifier, thus improving speed and classification accuracy. An experimental study by quantitative, qualitative and comparative evaluations shows that the proposed method can detect shadow robustly and remove the ‘cast’ part accurately from videos recorded by a static camera and under several constraints.


Cast shadow detection and removal foreground segmentation semi-supervised learning co-training technique 


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  1. 1.
    Wang, Y., Wang, S.: Shadow Detection of urban color aerial images based on partial differential equations. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII, part B2 (2008) Google Scholar
  2. 2.
    Dian, W.: The research on a mixtrue gausssian based clustering algorithm of make background model and supressing shadow. Northwestern Polytechnical University (2006) Google Scholar
  3. 3.
    Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: ICCV Frame-Rate Workshop, vol. 99, pp. 1–19 (1999) Google Scholar
  4. 4.
    Joshi, A.J., Papanikolopoulos, N.P.: Learning to Detect Moving Shadows in Dynamic Environments. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 2055–2063 (2008)CrossRefGoogle Scholar
  5. 5.
    Finlayson, G.D., Hordley, S.D., Drew, M.S.: Removing Shadows from Images Using Retinex. In: The 10th Color Imaging Conference: Color Science and Engineering Systems, Technologies, Applications, Scottsdale, Arizona, pp. 73–79 (2002)Google Scholar
  6. 6.
    Wang, J.M., Chung, Y.C., Chang, C.L.: Shadow detection and removal for traffic images. In: IEEE International Conference on Networking, Sensing and Control, vol. 1, pp. 649–654 (2004)Google Scholar
  7. 7.
    Liu, H., Yang, C., Shu, X., Wang, Q.: A new method of shadow detection based on edge information and HSV color information. In: 2nd Conference on Power Electronics and Intelligent Transportation System, vol. 1, pp. 286–289 (2009)Google Scholar
  8. 8.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting objects, shadows and ghosts in video streams by exploiting color and motion information. In: Proceedings 11th International Conference on Image Analysis and Processing, pp. 360–365 (2001)Google Scholar
  9. 9.
    Hammami, M., Jarraya, S.K., Ben-Abdallah, H.: On line Background Modeling For Moving Object Segmentation in Dynamic Scenes. Multimedia Tools and Applications Journal (2011); available in SpringerLink, appear first on-lineGoogle Scholar
  10. 10.
    Jarraya, K.S., Ghorbel, A., Chaouachi, A., Hammami, M.: ROADGUARD Highway Control and Management System. In: Proceedings of the International Conference on Computer Vision Theory and Applications, pp. 632–637 (2011)Google Scholar
  11. 11.
    Schohn, G., Cohn, D.: Less is More: Active Learning with Support Vector Machines. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 839–846 (2000)Google Scholar
  12. 12.
    Guggenberger, A.: Semi-supervised Learning With Support Vector Machines. Technischen Universität Wien (2008) Google Scholar
  13. 13.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)Google Scholar
  14. 14.
    Kononenko, I.: Estimating attributes: Analysis and extensions of relief. In: Proceeding ECML 1994 Proceedings of the European Conference on Machine Learning on Machine Learning, pp. 171–182 (1994)Google Scholar
  15. 15.
    Zhu, X.: Semi-Supervised Learning Literature Survey, New York. Technical Report (2008) Google Scholar
  16. 16.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kanfmann Publishers, Inc., Los Altos (1993)Google Scholar
  17. 17.
    Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 918–923 (2003)Google Scholar
  18. 18.
    Mikic, I., Cosman, P.C., Kogut, G.T., Trivedi, M.M.: Moving shadow and object detection in traffic scenes. In: 15th International Conference on Pattern Recognition, pp. 321–324 (2000)Google Scholar
  19. 19.
    Joshi, A.J., Atev, S., Masoud, O., Papanikolopoulos, N.: Moving Shadow Detection with Low- and Mid-Level Reasoning. In: IEEE International Conference on Robotics and Automation, pp. 4827–4832 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Salma Kammoun Jarraya
    • 1
  • Rania Rebai Boukhriss
    • 1
  • Mohamed Hammami
    • 2
  • Hanene Ben-Abdallah
    • 1
  1. 1.MIRACL-FSEGSfax UniversitySfaxTunisia
  2. 2.MIRACL-FSSfax UniversitySfaxTunisia

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