Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2006.13B.5.515

On-line Background Extraction in Video Image Using Vector Median  

Kim, Joon-Cheol (서남대학교 전지전자공학과)
Park, Eun-Jong (전북대학교 영상공학과)
Lee, Joon-Whoan (전북대학교 전자정보공학부)
Publication Information
The KIPS Transactions:PartB / v.13B, no.5, 2006 , pp. 515-524 More about this Journal
Abstract
Background extraction is an important technique to find the moving objects in video surveillance system. This paper proposes a new on-line background extraction method for color video using vector order statistics. In the proposed method, using the fact that background occurs more frequently than objects, the vector median of color pixels in consecutive frames Is treated as background at the position. Also, the objects of current frame are consisted of the set of pixels whose distance from background pixel is larger than threshold. In the paper, the proposed method is compared with the on-line multiple background extraction based on Gaussian mixture model(GMM) in order to evaluate the performance. As the result, its performance is similar or superior to the method based on GMM.
Keywords
background extraction; video surveillance system; vector median; on-line algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 J. Astola, P. Haaristo and Y. Neuvo, 'Vector Median Filters,' Proceeding of IEEE, Vol.78, No.4, pp.678-689, 1990   DOI   ScienceOn
2 엄경배, 한서원, 이준환, '혼합된 컬러 잡음하에서 컬러 영상 향상을 위한 조건적인 퍼지 클러스터 필터' 정보처리학회논문지 제6권 제12호, pp.3718-3726, 1999.
3 R. Cucchiara, C. Grana, M. Piccardi and A. Prati, 'Detecting Moving Objects, Ghosts, and Shadows in Video Streams,' IEEE Trans. PAMI, Vol.25, No.10, October, 2003   DOI   ScienceOn
4 C. Stauffer and W. E. L. Grimson, 'Adaptive Background Mixture Models for Real-Time Tracking,' Proc. Computer Vision and Pattern Recognition 99, Colorado   DOI
5 P.KaewTraKulPong and R. Bowden. 'An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow Detection,' 12nd. European Workshop on Advanced Video Based. Surveillance Systems, AVBS01, September, 2001
6 D. S. Lee, 'Effective Gaussian Mixture Learning for Video background Subtraction,' lEEE Tran. on PAMI, Vol.27, pp.827-832, 2005   DOI   ScienceOn
7 B. Gloyer, H.K. Aghajan, K. Y. Siu and T. Kailath, 'Video-Based Freeway Monitoring System Using Recursive Vehicle Tracking,' Proc. SPIE Symp. Electronic Imaging: Imag and Video Processing, 1995   DOI
8 Z. Hou and C. Han, 'A Background Reconstruction Algorithm based on Pixel Intensity Classification in Remote Video Surveillance,' Proc. of 7th Conf. Information Fusion, June, 2004
9 D. Wang, 'A Novel Probability Model for Background Maintenance and Subtraction,' 15th Conf. Vision Interface May, 2002
10 S. S. Cheung and C. Kamath, 'Robust techniques for background subtraction in urban traffic video,' Proc. of SPIE Visuual Communications and Image Processing, 2004   DOI
11 B.P.L. LO and S.A. Velastin, 'Automatic Congestion Detection System for Underground Platforms,' Proc. Symp. Intelligence Multimedia, Video, and Speech Processing, pp.158-161, 2000   DOI
12 I. Haritaoglu, D. Harwood and L. S. Davis, 'W4:Real-Time Surveillance of people and Their Activities,' IEEE Tranns. PAMI, Vol.22, No.8, pp.809-830, 2000   DOI   ScienceOn
13 N. Frieman and S. Russell, 'Image segmentation In video sequences: A probabilistic approach,' Proc. 13th Cant. Uncertainty in Artificial Intelligence, Aug., 1997
14 조태훈, 최영규 '다중 배경 분포를 이용한 움직임 검출', 정보처리학회논문지 제8권-B권 제4호, pp.381. 2000   과학기술학회마을