Abstract
Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a boosting discriminative model for moving cast shadow detection. Firstly, color invariance subspace and texture invariance subspace are obtained by the color and texture difference between current image and background image; then, boosting is selected based on theses subspaces to discriminate cast shadow from moving objects; finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields for accurate image segmentation through graph cut. Results show that the proposed method excels classical method both in indoor and outdoor scene.
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References
Horprasert, T., Harwood, D., Davis, L.: A statistical approach for real-time robust background subtraction and shadow detection. In: Proc. of IEEE ICCV Frame-rate Workshop (1999)
Mikic, I., Cosman, P., Kogut, G., Trivedi, M.: Moving shadow and object detection in traffic scenes. In: Proceedings of the 15th International conference on Pattern Recognition, Barcelona, vol. 1, pp. 321–324 (2000)
Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts and shadow in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1337–1342 (2003)
Salvador, E., Cavallaro, A., Ebrahimi, T.: Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding 95, 238–259 (2004)
Elgammal, A., Harwood, D., Davis, L.S.: Elgammal a.background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of IEEE 90, 1151–1163 (2002)
Stauder, J., Mech, R., Ostermann, J.: Detection of moving cast shadows for object segmentation. IEEE Transactions on Multimedia 1, 65–76 (1999)
Wang, Y., Loe, K.F., Wu, J.K.: A dynamic conditional random field model for foreground and shadow segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 279–289 (2006)
Nadimi, S., Bhanu, B.: Physical models for moving shadow and object detection in video. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 1079–1087 (2004)
Porikli, F., Thornton, J.: Shadow flow: A recursive method to learn moving cast shadow. In: IEEE International Conference on Computer Vision (ICCV), Beijing (2005)
Zhao, T., Nevatia, R.: Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1208–1221 (2004)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min- cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1222–1239 (2001)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 147–159 (2004)
Klinker, G., Shafer, A., Kanada, T.: A physical approach to color image understanding. International Journal of Computer Vision 4, 7–38 (1990)
Prati, A., Mikic, I., Trivedi, M., Cucchiara, R.: Detecting moving shadow: Algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 918–923 (2003)
Kumar, S., Hebert, M.: Discriminative random fields: A discriminative framework for contextual interaction in classification. In: IEEE International Conference on Computer Vision (ICCV) (2003)
Schapire, R.: The boosting approach to machine learning: An overview. In. In: Proc. MSRI workshop on Nonlinear Estimation and Classification (2001)
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
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© 2007 Springer-Verlag Berlin Heidelberg
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Zha, Y., Chu, Y., Bi, D. (2007). A Boosting Discriminative Model for Moving Cast Shadow Detection. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_20
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DOI: https://doi.org/10.1007/978-3-540-74198-5_20
Publisher Name: Springer, Berlin, Heidelberg
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