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http://dx.doi.org/10.9708/jksci.2014.19.10.055

An Adaptive Background Formation Algorithm Considering Stationary Object  

Jeong, Jongmyeon (Dept. of Computer Engineering, Mokpo National Maritime University)
Publication Information
Journal of the Korea Society of Computer and Information / v.19, no.10, 2014 , pp. 55-62 More about this Journal
Abstract
In the intelligent video surveillance system, moving objects generally are detected by calculating difference between background and input image. However formation of reliable background is known to be still challenging task because it is hard to cope with the complicated background. In this paper we propose an adaptive background formation algorithm considering stationary object. At first, the initial background is formed by averaging the initial N frames. Object detection is performed by comparing the current input image and background. If the object is at a stop for a long time, we consider the object as stationary object and background is replaced with the stationary object. On the other hand, if the object is a moving object, the pixels in the object are not reflected for background modification. Because the proposed algorithm considers gradual illuminance change, slow moving object and stationary object, we can form background adaptively and robustly which has been shown by experimental results.
Keywords
Background formation; Image subtraction; Difference Image; Video surveillance system;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 R. Collins, A. Lipton and T. Kanade "Introduction to the special section on video surveillance", IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp.745-746 2000.   DOI   ScienceOn
2 B. Lee, M. Hedley, "Background estimation for video surveillance", IVCNZ, pp. 315-320, 2002.
3 C. Staffer, W. E. L. Grimson, "Adaptive background mixture models for real-time tracking", International Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 23-25, 1999.
4 Radke RJ, Andra S, Al-Kofahi O, Roysam B., "Image Change Detection Algorithms: A systematic survey," IEEE Trans on Image processing, Vol. 14(3), pp. 294-307, Mar. 2005.   DOI   ScienceOn
5 P. Kumar, S. Ranganath, and W. Huang, "Queue based fast background modelling and fast hysteresis thresholding for better foreground segmentation," The 2003 Joint Conferenece of the Fourth ICICS and PCM, Vol. 2, pp. 15-18, 2003.
6 I. Haritaoglu, D. Harwood, and L. Davis, "W4: Real-time surveillance of people and their activities," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.22, pp. 809-830, Aug. 2000.   DOI   ScienceOn
7 Z. Tang and Z. Miao, "Fast background subtraction and shadow elimination using improved gaussian mixture model," IEEE International Workshop on Haptic Audio Visual Environments and their Applications, pp. 38-41, Oct. 2007.
8 D. Lee, "Effective gaussian mixture learning for video background subtraction," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.27, No.5, pp.827-832, May 2005.   DOI   ScienceOn
9 A. Shimada, T. Tanaka, D. Arita and R. Taniguchi, "Spatial-temporal integration of adaptive gaussian mixture background models," Proc. of FCV 2008, pp142-147, Jan. 2008.
10 J. Lee and O. Chae, "A study of background edge generation for moving object detection under moving camera," Journal of Korean Society of Computer and Information, Vol.6, No.44, p.151-156, Dec. 2006.
11 H. Kim, Y. Lee, T. Song, B. Ku and H. Ko, "Improved block-based background modeling using adaptive parameter estimation", Journal of Korean Society of Computer and Information, Vol.16, No.4, pp.73-82, Apr. 2011.
12 T. Hosaka, T. Kobayashi, and N. Otsu, "Object detection using background subtraction and foreground motion estimation," IPSJ Trans. on Computer Vision and Applications, Vol.3, pp. 9-20, Mar. 2011.   DOI   ScienceOn
13 T. Bouwmans, "Traditional and recent approaches in background modeling for foreground detection: An overview," Computer Science Review, II-I2, pp. 31-66, 2014.