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Robust moving shadow detection with hierarchical mixture of MLP experts

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Abstract

Detection and elimination of the shadows of moving objects in video sequences have been one of the major challenges in tracking applications. Since moving shadows cannot be removed from foreground by motion-based background subtraction methods, they lead to confusion and error in moving object tracking. In this paper, a novel classification method based on hierarchical mixture of experts learning for detecting shadows from foreground is proposed. A hierarchical mixture of MLP experts method (HMME) with semi-supervised teacher-directed learning (SSP-HMME) is used. It contains a two-level mixture of experts (ME) system. The main superiority of this method is that it is more robust than state-of-the-art methods in all types of indoor and outdoor environments. The robustness is against the number of light sources, illumination conditions, surface orientations, object sizes, etc., and it is estimated using accuracy rates. The video set has been collected from 7 different datasets. The results of experiments in outdoor and indoor environments show the validity of the method in the improvement on the accuracy of both detection and discrimination rate for moving shadows in video sequences. The results of the experiments show the accuracy rate of 89 % in average in different indoor and outdoor environmental conditions that is about 6 % better than current state-of-the-art methods.

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References

  1. Joshi A.J., Papanikolopoulos N.P.: Learning to detect moving shadows in dynamic environments. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 2055–2063 (2008)

    Article  Google Scholar 

  2. So, A.W.K., Wong, K.K.Y., Chung, R.H.Y., Chin, F.Y.L.: Shadow detection for vehicles by locating the object-shadow boundary. In: Conference on Signal and Image Processing (2005)

  3. Girisha. R, Murali. S.: Segmentation of motion objects from surveillance video sequences using partial correlation. In: ICIP (2009)

  4. Zhang, W., Fang, X.Z., Xu, Y.: Detection of moving cast shadows using image orthogonal transform. In: Proceedings of IEEE International Conference on Pattern Recognition, vol. 1, pp. 626–629 (2006)

  5. Prati A., Mikic I., Trivedi M.M., Cucchiara R.: Detecting moving shadows: algorithms and evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 918–923 (2003)

    Article  Google Scholar 

  6. Jiang, C., Ward, M.O.: Shadow identification. In: Proceedings of Computer Vision and Pattern Recognitin, Champaign, Illinois, pp. 606–612 (1992)

  7. Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence (1997)

  8. Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: International Conference of Computer Vision. Frame-rate Workshop, Kerkyra, Greece (1999)

  9. Mikic, I., Cosman, P.C., Kogut, G.T., Trivedi, M.M.: Moving shadow and object detection in traffic scenes. In: Proceedings of IEEE International Conference on Pattern Recognition. vol. 1, pp. 321–324 (2000)

  10. Martel-Brisson, N., Zaccarin, A.: Moving cast shadow detection from a Gaussian mixture shadow model. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 643–648 (2005)

  11. Salvador E., Cavallaro A., Ebrahimi T.: Cast shadow segmentation using invariant color features. Comput. Vis. Image Underst. 95, 238–259 (2004)

    Article  Google Scholar 

  12. Martel-Brisson, N., Zaccarin, A.: Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation. In: CVPR (2008)

  13. Siala, K., Chakchouk, M., Chaieb, F., Besbes, O.: Moving shadow detection with support vector domain description in the color ratios space. In: ICPR, pp. 384–387 (2004)

  14. Benedek, C., Szir’anyi, T.: Bayesian foreground and shadow detection in uncertain frame rate surveillance videos. In: IEEE Trans. Image Process. pp. 608–621 (2008)

  15. Huerta, I., Holte, M., Moeslund, T., Gonzà àlez, J.: Detection and removal of chromatic moving shadows in surveillance scenarios. In: 12th International Conference on Computer Vision, Kyoto, Japan (2009)

  16. Zhang, W., Wu, Q.M.J.: Moving shadow detection based on normalized eigenvalue of wishart matrix. In: International Conference on Mechatronics and Automation, Xi’an, China, 4–7 August 2010

  17. Mori, H., Charkari, N.M.: Shadow and rhythm as sign patterns of obstacle detection. In: International Symposium on Industrial Electronics, pp. 271–277 (1993)

  18. Stauder J., Mech R., Ostermann J.: Detection of moving cast shadows for object segmentation. IEEE Trans. Multimed. 1(1), 65–76 (1999)

    Article  Google Scholar 

  19. Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting objects, shadows and ghosts in video streams by exploiting color and motion information. In: Proceedings of the IEEE International Conference on Image Analysis and Processing (2001)

  20. Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Improving shadow suppression in moving object detection with HSV color information. In: Proceedings of the IEEE International Conference on Intelligent Transportation Systems (2001)

  21. Leone, A., Distante, C.: Shadow detection for moving objects based on texture analysis. In: Pattern Recognition, pp. 1222–1233 (2007)

  22. Wang, J.M., Chung, Y.C., Chang, C.L., Chen, S.W.: Shadow detection and removal for traffic images. In: IEEE International Conference on Networking, Sensing and Control, vol. 1, pp. 649–654 (2004)

  23. Xiao M., Han C.-Z., Zhang L.: Moving shadow detection and removal for traffic sequences. Int. J. Autom. Comput. 4(1), 38–46 (2007)

    Article  Google Scholar 

  24. Song K.-T., Tai J.-C.: Image-based traffic monitoring with shadow suppression. Proc. IEEE 95(2), 413–426 (2007)

    Article  Google Scholar 

  25. Yang M.-T., Lo K.H., Chiang C.C., Tai W.K.: Moving cast shadow detection by exploiting multiple cues. IET Image Process. 2(2), 95–104 (2008)

    Article  Google Scholar 

  26. Huang, J.-B., Chen, C.-S.: Moving cast shadow detection using physics-based features. In: CVPR, pp. 2310–2317 (2009)

  27. Moro, A., Terabayashi, K., Umeda, K.: Detection of moving objects with removal cast shadow and periodic changes using stereo vision. In: ICPR, pp. 328–331 (2010)

  28. Madsen, C.B., Moeslund, T.B., Pal, A., Balasubramanian, S.: Shadow detection in dynamic scenes using dense stereo information and an outdoor illumination model. In: Dyn3D, pp. 110–125 (2009)

  29. Sun, B., Li, S.: Moving cast shadow detection of vehicle using combined color models. In: Proceedings of the Chinese Conference on Pattern Recognition, vol. 1, pp. 503–507 (2010)

  30. Wang, C., Huang, L., Rosenfeld, A.: Detecting clouds and cloud shadows on aerial photographs. In: PRL 12, 55–64 (1991)

  31. Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., Russell, S.: Towards robust automatic traffic scene analysis in real-time. In: Proceedings of ICPR’94, pp. 126–131 (1994)

  32. Xu D., Li X., Liu Z., Yuan Y.: Cast shadow detection in video segmentation. Pattern Recognit. Lett. 26(1), 91–99 (2005)

    Article  Google Scholar 

  33. Hsieh J.-W., Yu S.-H., Chen Y.-S., Hu W.-F.: Automatic traffic surveillance system for vehicle tracking and classification. IEEE Trans. Intell. Transp. Syst. 7(2), 175–187 (2006)

    Article  Google Scholar 

  34. Hsieh J.W., Hu W.F. et al.: Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image Vis. Comput. 21(6), 505–516 (2011)

    Article  Google Scholar 

  35. Jacobs R.A., Jordan M.I., Nowlan S.J., Hinton G.E.: Adaptive mixtures of local experts. Neural Comput. 3, 79–87 (1991)

    Article  Google Scholar 

  36. Moerland, P.: Classification using localized mixtures of experts. In: Artificial Neural Networks, Conference Publication No. 470, 7–10 Sept 1999

  37. Mangeas, M., Weigend, A.S., Muller, C.: Forecasting electricity demand using nonlinear mixture of experts. In: Proceedings of WCNN’95, World Conference on Neural Networks, vol. 2, pp. 48–53 (1995)

  38. Titsias M.K., Likas A.: Mixture of experts classification using a hierarchical mixture model. Neural Comput. 14, 2221–2244 (2002)

    Article  MATH  Google Scholar 

  39. Goldman, S., Rivest, R., Schapire, R.: Learning binary relations and total orders. In: Proceedings of the 30th Annual IEEE Symposium on Foundations of Computer Science, pp. 46–51 (1989)

  40. Ebrahimpour R., Kabir E., Yousefi M.R.: Improving mixture of experts for view-independent face recognition using teacher-directed learning. Mach. Vis. Appl. 22, 421–432 (2011)

    Article  Google Scholar 

  41. Ebrahimpour R., Kabir E., Yousefi M.R.: Teacher-directed learning in view-independent face recognition with mixture of experts using overlapping eigenspaces. Comput. Vis. Image Underst. 111, 195–206 (2008)

    Article  Google Scholar 

  42. Broujeni, H.R.S., Charkari, N.M.: A new background subtraction method in video sequences based on temporal motion windows. In: International Conference on IT, March 2009

  43. http://www.asaatid.com/shayegh/datasets/shadow

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Hamid Shayegh Boroujeni & Nasrollah Moghadam Charkari

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  1. Hamid Shayegh Boroujeni
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  2. Nasrollah Moghadam Charkari
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Correspondence to Hamid Shayegh Boroujeni.

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Boroujeni, H.S., Charkari, N.M. Robust moving shadow detection with hierarchical mixture of MLP experts. SIViP 8, 1291–1305 (2014). https://doi.org/10.1007/s11760-012-0358-5

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  • DOI: https://doi.org/10.1007/s11760-012-0358-5

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