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Moving Object Detection for Video Surveillance using Different Color Spaces
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 118 - Number 13 |
| Year of Publication: 2015 |
| Authors: Adesh Hardas, Dattatray Bade, Vibha Wali |
10.5120/20808-3553
|
Adesh Hardas, Dattatray Bade, Vibha Wali . Moving Object Detection for Video Surveillance using Different Color Spaces. International Journal of Computer Applications. 118, 13 ( May 2015), 39-43. DOI=10.5120/20808-3553
Abstract
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, during background subtraction pixels belonging to shadow are misclassified as foreground object. Moving cast shadow associated with moving object also gets detected making it challenge for video surveillance. 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. However, it is difficult to determine the optimal color space in which to remove shadow. In this paper, we study the features of moving object and shadow in different color spaces to solve the problem.
References
- W. M. Hu, T. Tan, et al. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics - PART C: Applications and Reviews, 34(3):334–351, 2004.
- A. Prati et al. Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25:918–923, 2003.
- P. Kumar, K. Sengupta, et al. A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system. In Proceedings of IEEE the 5th International Conference on Intelligent Transportation Systems, pp. 100–105, 2002.
- T. Horprasert, D. Harwood, and L. S. Davis. A robust background subtraction and shadow detection. Proc. ACCV, 2000.
- R. Cucchiara. Improving shadow suppression in moving object detection with hsv color information. In Proceedings of IEEE Conference on Intelligent Transportation Systems, pp. 334–339, 2001.
- 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.
- Shih-Chieh Wang, Te-Feng Su and Shang-Hong Lai"Detecting Moving Object From Dynamic Background with Shadow Removal"ICASSP2011 IEEE
- A. Elgammal, D. Hardwood, and L. Davis. Non-parametric model for background subtraction. In ECCV00, pages 751–767, 2000.
- 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.
- Jianhua Ye, Tao Gao, Jun Zhang, "Moving object detection with background subtraction and shadow removal" in the proceedings of International Conference on Fuzzy Systems and Knowledge Discovery 978-1-4673-0024-7/10 ©2012 IEEE 2012.
- C. Jiang and M. O. Ward, ?Shadow identification,? in Proceedings of IEEE Int'l Conference on Computer Vision and Pattern Recognition, , 1992 ,pp. 606–612.
- Sen-Ching S. Cheung, Chandrika Kamath, 2005. Robust Background Subtraction with Foreground Validation for Urban Trafic Video, EURASIP Journal on Applied Signal Processing, 14: 2330-2340, Hindawi Publishing Corporation.
- McFarlane and C. Schofield. Segmentation and tracking of piglets in images. MVA, 8:187–193, 1995.
- J. Stauder, R. Mech, and J. Ostermann, ?Detection of moving cast shadows for object segmentation,?in proceedings of IEEE Transactions on Multimedia, vol. 1, no. 1, Mar. 1999, pp. 65-76.
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