Abstract

This paper deals with an efficient background subtraction of image/frames of video by improving the execution speed, accuracy and reduce the usage of memory. Three important techniques are applied to improve the efficiency: superpixel extraction, canny edge detection and fuzzy c means. On applying the above three methods sequentially, the background of image/video can be segmented from foreground object accurately. The first method reduces the processing data more than 75%. Canny edge detection is an optimized method to detect edges. Fuzzy c means works well and good to segment the overlapped objects in an image/video.

References

• X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. 9th Int. Conf. Computer Vision, volume 1, pages 10-17, 2003. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed. , vol. 2. Oxford: Clarendon, 1892, pp. 68–73.
• G. Mori, X. Ren, A. Efros, and J. Malik, Recovering Human Body Configurations: Combining Segmentation and Recognition, IEEE Computer Vision and Pattern Recognition, 2004.
• G. Mori, Guiding Model Search Using Segmentation, IEEE International Conference on Computer Vision, 2005.
• Cheung, S. -C. and C. Kamath, "Robust Background Subtraction with Foreground Validation for Urban Traffic Video," EURASIP Journal on Applied Signal Processing, Volume 14, pp 1-11, 2005. UCRL-JRNL-201916.
• Li Cheng, Minglun Gong, Dale Schuurmans and Terry Caelli, "Real Time Discriminative Background Subtraction", in IEEE Transactions on Image Processing, Vol 20, No. 5, pages 1401-1414, May 2011.
• Sergei Azernikov. Sweeping solids on manifolds. In Symposium on Solid and Physical Modeling, pages 249–255, 2008.
• John Canny. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-8(6):679–698, Nov. 1986.
• F. Mai, Y. Hung, H. Zhong, and W. Sze. A hierarchical approach for fast and robust ellipse extraction. Pattern Recognition, 41(8):2512– 2524, August 2008.
• Thomas B. Moeslund. Image and Video Processing. August 2008.
• C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981.
• J. K. Udupa and S. Samarasekera, "Fuzzy connectedness and object definition: Theory, algorithm and applications in image segmentation," Graphical Models Image Processing, vol. 58, no. 3, pp. 246-261, 1996.
• J. C. Dunn (1973): "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3: 32-57.
• J. C. Bezdek (1981): "Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York.
• C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 2: 252, 1999.
• Z. Zivkovic, "Improved adaptive gaussian mixture model for background subtraction", in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. , volume 2, pages 28-31 Vol. 2, 2004.
• P. Kaewtrakulpong and R. Bowden, "An improved adaptive background mixture model for realtime tracking with shadow detection", in Proceeding 2nd European Workshop on Advanced Video Based Surveillance Systems, Computer Vision and Distributed Processing, 2001.
• O. Javed, K. Shafique, and M. Shah, "A hierarchical approach to robust background subtraction using color and gradient information", in Workshop on Motion and Video Computing, 2002, pages 22-27, 2002.
• Hong-xun Zhang and De Xu, "Fusing color and gradient features for background model", in 8th International Conference on Signal Processing, 2006.
• Li Cheng, Minglun Gong, Dale Schuurmans and Terry Caelli, "Real Time Discriminative Background Subtraction", in IEEE Transactions on Image Processing, Vol 20, No. 5, pages 1401-1414, May 2011.
• Y. Ivanov, A. Bobick, and J. Liu, "Fast lighting independent background subtraction", in IEEE Workshop on Visual Surveillance, pages 49-55, 1998.
• Ser-nam Lim, Anurag Mittal, Larry S. Davis, and Nikos Paragios, "Fast illumination-invariant background subtraction using two views: Error analysis, sensor placement and applications", IEEE Conference on Computer Vision and Pattern Recognition (CVPR'05), 1: 1071-1078, 2005.
• Hanzi Wang and D. Suter, "A re-evaluation of mixture of Gaussian background modeling [video signal processing applications]", in IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP '05), 2005, volume 2, pages 1017-1020, 2005.
• M. Piccardi, "Background subtraction techniques: a review", in IEEE International Conference on Systems, Man and Cybernetics, 2004, volume 4, pages3099-3104,2005.