지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국조명·전기설비학회 조명·전기설비학회논문지 Journal of the Korean Institute of Illuminating and Electrical Installation Engineers Vol.21 No.4
- 발행연도
- 2007.5
- 수록면
- 11 - 18 (8page)
이용수
초록· 키워드
오류제보하기
A moving object detection algorithm for surveillance video is here proposed which employs background initialization based on two-stage median filtering and a background updating method based on state transition diagram.
In the background initialization, the spatiotemporal similarity is measured in the subinterval. From the accumulated difference between the base frame and the other frames in a subinterval, the regions affected by moving objects are located. The median is applied over the subsequence in the subinterval in which regions share similarity. The outputs from each subinterval are filtered by a two-stage median filter.
The background of every frame is updated by the suggested state transition diagram. The object is detected by the difference between the current frame and the updated background.
The proposed method showed good results even for busy, crowded sequences which included moving objects from the first frame.
In the background initialization, the spatiotemporal similarity is measured in the subinterval. From the accumulated difference between the base frame and the other frames in a subinterval, the regions affected by moving objects are located. The median is applied over the subsequence in the subinterval in which regions share similarity. The outputs from each subinterval are filtered by a two-stage median filter.
The background of every frame is updated by the suggested state transition diagram. The object is detected by the difference between the current frame and the updated background.
The proposed method showed good results even for busy, crowded sequences which included moving objects from the first frame.
목차
Abstract
1. Introduction
2. Background Initialization
3. Background Update
4. Moving Object Separation
5. Experimental Results and Analysis
6. Conclusion
References
Blography
참고문헌 (5)
참고문헌 신청
D. Gutchess, et al., "A Background Model Initialization Algorithm for Video Surveillance," Proceedings of Eighth IEEE International Conference on Computer Vision, 2001, ICCV 2001, Vol.1, pp.733-740, July, 2001.
C. Stauffer, W.E.L. Grimson, "Adaptive Background Mixture Models for Real-time Tracking", Proc.IEEE Conference on Computer Vision & Pattern Recognition. Vo.2, pp.246-252, June, 1999.
Marko Heikkila, and Matti Pietikainen, "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.28, No.4, pp.657-662, April, 2006.
Hanzi Wang and David Suter, "Background Initialization with A New Robust Statistical Approach," Proceedings of 2nd Joint IEEE International Workshop on VS-PETS, pp.153-159,October,2005.
C.R. Wren, et al., "Pfinder: Real-time Tracking of the Human body," PAM 19(7): pp.780-785, 1997.
C. Stauffer, W.E.L. Grimson, "Adaptive Background Mixture Models for Real-time Tracking", Proc.IEEE Conference on Computer Vision & Pattern Recognition. Vo.2, pp.246-252, June, 1999.
Marko Heikkila, and Matti Pietikainen, "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.28, No.4, pp.657-662, April, 2006.
Hanzi Wang and David Suter, "Background Initialization with A New Robust Statistical Approach," Proceedings of 2nd Joint IEEE International Workshop on VS-PETS, pp.153-159,October,2005.
C.R. Wren, et al., "Pfinder: Real-time Tracking of the Human body," PAM 19(7): pp.780-785, 1997.
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