In many vision based application identifying moving objects is important and critical task. For different computer vision application Background subtraction is fast way to detect moving object. Background subtraction separates the foreground from background. However, background subtraction is unable to remove shadow from foreground. Moving cast shadow associated with moving object also gets detected making it challenge for video surveillance. The shadow makes it difficult to detect the exact shape of object and to recognize the object. Now days many methods are available for background subtraction. The core of background subtraction is background modeling. Gaussian Mixture model is good balance between accuracy and complexity. For better result post processing is done to output of Gaussian Mixture model. The experimental results give good performance for the proposed method.

1. P. Kumar, and A. Mittal,?Study of robust and intelligent surveillance in visible and multimodal framework,? informatica, Vol. 31, 2007 pp. 447-461.
2. M. Piccardi, T. Jan, "Efficient mean-shift backgonmd subtraction", to appear in Proc. of IEEE 2004 KIP, Singapore, Oct. 2004.
3. T. Horprasert, D. Harwood, and L. S. Davis, "A statistical approach for real time robust background subtraction and shadow detection. " In the proceedings of IEEE ICCV'99 FRAME-RATE workshop,1999.
4. R. Cucchiara, C. Grana, M. Piccardi, A. Prati, and S. Sirotti, "Improving shadow suppression in moving object detection with HSV color information," in Proceedings of IEEE Int'l Conference on Intelligent Transportation Systems, Aug. 2001, pp. 334–339.
5. A. Prati, Ivana Miki, Mohan M. Trivedi, Rita Cucchiara, "Detecting Moving Shadows: Algorithms and Evaluation" IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 918-923, July 2003.
6. http://en. wikipedia. org/wiki/Kalman_filter.
7. C. Stauffer, WEL. Grimson, "Adaptive Background Mixture Models for Real- Time Tracking," IEEE Computer Society Conf. on Computer Vision and Pattern Recognition CVPR,; vol. 2, pp. 246-252, 1999.
8. A. Sanin, C. Sanderson, B. C. Lovell. Shadow Detection: A Survey and Comparative Evaluation of Recent Methods. Pattern Recognition, Vol. 45, No. 4, pp. 1684–1695, 2012.
9. Zhan Chaohui Duan Xiaohui, Xu Shuoyu Song Zheng Luo Min, 2007. An improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection", Fourth International Conference on Image and Graphics 0-7695-2929-1/07 $25. 00, 2007 IEEE.
10. E. Salvador, A. Cavallaro, and T. Ebrahimi. Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding, 95(2):238–259, 2004.
11. S. C. Cheung and C. Kamath, "Robust techniques for background subtraction in urban traffic video," Video Communications and Image Processing, SPIE Electronic Imaging, UCRL Conf. San Jose, vol. 200706, Jan 2004.
12. C. -T. Chen, C. -Y. Su, and W. -C. Kao. An enhanced segmentation on vision-based shadow removal for vehicle detection. In International Conference on Green Circuits and systems, pages 679-682, 2010.

Background Subtraction Gaussian Mixture Model Shadow Removal Rgb Color Space