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
- 발행연도
- 2019.5
- 수록면
- 149 - 156 (8page)
이용수
초록· 키워드
오류제보하기
The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network.
The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.
The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.
목차
Abstract
I. Introduction
II. Related works
III. Proposed Scheme
IV. Conclusion
REFERENCES
참고문헌 (14)
참고문헌 신청
이규웅 / 2014 / 움직임 감지를 이용한 네트워크 카메라 기반 영상보안 시스템 구현 / 한국인터넷방송통신학회 논문지 14(1):169~177
서진근 / 2013 / A TREATMENT OF CONTACT DISCONTINUITY FOR CENTRAL UPWIND SCHEME BY CHANGING FLUX FUNCTIONS / Journal of the Korean Society for Industrial and Applied Mathematics 17(1):47~54
김형훈 / 2015 / 배경의 통계정보를 이용한 효율적인 자동 전경 움직임 탐지 / 한국컴퓨터정보학회논문지 20(9):121~128
S. Ferrari / 2005 / Smooth function approximation using neural networks / IEEE Trans Neural Network 16:24~38
OpenCV Open Source Computer Vision
서진근 / 2013 / A TREATMENT OF CONTACT DISCONTINUITY FOR CENTRAL UPWIND SCHEME BY CHANGING FLUX FUNCTIONS / Journal of the Korean Society for Industrial and Applied Mathematics 17(1):47~54
김형훈 / 2015 / 배경의 통계정보를 이용한 효율적인 자동 전경 움직임 탐지 / 한국컴퓨터정보학회논문지 20(9):121~128
S. Ferrari / 2005 / Smooth function approximation using neural networks / IEEE Trans Neural Network 16:24~38
OpenCV Open Source Computer Vision
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UCI(KEPA) : I410-ECN-0101-2019-004-000787874