Moving Object Detection Based on Edge Difference and Contour Matching

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This paper presents an improved method to detect moving object and obtain the relative accurate location. First we detect the edge difference of continuous frames. Then we utilize the contour matching to find the edge pairs in order to reach a good detection of the moving object and location. The extensive experiments show that our method is robust and efficient to the moving object detection.

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1863-1867

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June 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Zhan Chaohui, Duan Xiaohui, Xu Shuoyu, Song Zheng, and Luo Min, An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection, Fourth International Conference On image and Graphics, 2007, pp.519-523.

DOI: 10.1109/icig.2007.153

Google Scholar

[2] G. Bradski, A. Kaehler, Learning OpenCV, " O, Reilly Media, (2008).

Google Scholar

[3] Hu. M. Visual pattern recognition by moment invariants[ J].

Google Scholar

[4] Rita Cucchiara, Costantino Grana, Massimo Piccardi, and Andrea Prati, Detecting Moving Objects, Ghosts, and Shadows in Video Streams, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 10, OCTOBER 2003. pp.1337-1342.

DOI: 10.1109/tpami.2003.1233909

Google Scholar

[5] Ren Mingwu, and Sun Han, A Practical Method for Moving Target Detection Under Complex Background,. Computer Engineering, Oct 2005, pp.33-34.

Google Scholar

[6] J. L. Barron, D. J. Fleet, and S. S. Beauchemin, Performance of optical flow techniques, " Int, l J. Computer Vision, vol. 12(1), p.43–77, February (1994).

DOI: 10.1007/bf01420984

Google Scholar

[7] M. Yokoyama, T. Poggio, A Contour-Based Moving Object Detection and Tracking", ICCCN , 05 Proceedings of the 14th International Conference on Computer Communications and Networks.

Google Scholar

[8] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, Wallfl ower: Principles and practice of background maintenance, Proceedings of the 7th IEEE International Conference on Computer Vision (p.255–261), (1999).

DOI: 10.1109/iccv.1999.791228

Google Scholar

[9] A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara. Detecting moving shadows: Algorithms and evaluation,. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25: 918–923, (2003).

DOI: 10.1109/tpami.2003.1206520

Google Scholar

[10] L. Li, W. Huang, I. Y. H. Gu, and Q. Tian, Foreground object detection in changing background based on color co-occurrence statistics, in Proc. IEEE Workshop on Applications of Computer Vision, (2002).

DOI: 10.1109/acv.2002.1182193

Google Scholar

[11] C. Stauffer and W. E. L. Grimson, Adaptive background mixture models for real-time tracking, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, (1999).

DOI: 10.1109/cvpr.1999.784637

Google Scholar

[12] A. Elgammal, D. Harwood, and L. Davis, Non-parametric model for background subtraction, in Proc. European Conf. Computer Vision, vol. II, May 2000, p.751–767.

DOI: 10.1007/3-540-45053-x_48

Google Scholar

[13] T. Horprasert, D. Harwood, and L. Davis. A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection, Proceedings of IEEE Frame-Rate Workshop, Kerkyra, Greece, (1999).

Google Scholar

[14] Cui Yuyong, Zeng Zhiyuan, Liu liu, An object detection algorithm based on the cloud model, Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009, v1, pp.910-912.

DOI: 10.1109/cso.2009.478

Google Scholar