Journal of Satellite, Information and Communications (한국위성정보통신학회논문지)

Korea Society of Satellite Technology (한국위성정보통신학회)
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Flame detection algorithm using adaptive threshold in thermal video

적응 문턱치를 이용한 열영상 화염 검출 알고리즘

  • 정수영 (공주대학교 전기전자제어공학부) ;
  • 김원호 (공주대학교 전기전자제어공학부)
  • Received : 2014.11.03
  • Accepted : 2014.12.01
  • Published : 2014.12.31
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Abstract

This paper proposed an adaptive threshold method for detecting flame candidate regions in a infrared image and it adapts according to the contrast and intensity changes in the image. Conventional flame detection systems uses fixed threshold method since surveillance environment does not change, once the system installed. But it needs a adaptive threshold method as requirements of surveillance system has changed. The proposed adaptive threshold algorithm uses the dynamic behavior of flame as featured parameter. The test result is analysed by comparing test result of proposed adaptive threshold algorithm and conventional fixed threshold method. The analysed data shows, the proposed method has 91.42% of correct detection rate and false detection is reduced by 20% comparing to the conventional method.

본 논문은 적외선 열영상에서 영상의 밝기와 대비 변화에 따라 적응적으로 화염 후보 영역을 검출하기 위한 적응 문턱치를 제안한다. 현장에 사용 되고 있는 화재 검출 시스템은 카메라의 설치 장소에 따라 얻어지는 영상의 밝기나 대비의 변화가 발생 하여 고정된 문턱치를 적용하는 화재 검출 알고리즘의 성능이 변화하게 되므로 환경에 적응적인 문턱치가 필요하다. 제안하는 적응 문턱치를 이용한 화염 검출 알고리즘은 화염의 특성인 온도와 동적임 특성을 분석하여 화염을 검출 한다. 실험을 위해 고정 문턱치를 이용한 화염 검출 알고리즘과 비교 하였으며 제안된 적응 문턱치를 이용한 화염 검출 알고리즘은 화염 검출률 91.42%이며 고정 문턱치를 적용 하였을 때 보다 오검출률을 약 20%가 감소한다. 그리고 영상의 밝기와 대비 변화에 의한 검출 결과가 일정함을 보여 준다.

Keywords

References

  1. Arrue, B.C.; Ollero, A.; Matinez de Dios, J.R., "An intelligent system for false alarm reduction in infrared forest-fire detection", Intelligent Systems and their Applications, IEEE , vol.15, no.3, pp.64,73, May/Jun 2000.
  2. A. Ollero, B.C. Arrue, J.R. Martinez, J.J. Murillo, "Techniques for reducing false alarms in infrared forest-fire automatic detection systems", Control Engineering Practice, Volume 7, Issue 1, January 1999, Pages 123-131. https://doi.org/10.1016/S0967-0661(98)00141-5
  3. Bosch, I.; Gomez, S.; Vergara, L.; Moragues, J., "Infrared image processing and its application to forest fire surveillance," Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on , vol., no., pp.283,288, 5-7 Sept. 2007.
  4. Bosch, I.; Gomez, S.; Vergara, L., "Automatic Forest Surveillance Based on Infrared Sensors," Sensor Technologies and Applications, 2007. SensorComm 2007. International Conference on , vol., no., pp.572,577, 14-20 Oct. 2007.
  5. Won-Ho Kim; Seung-Kyeom Kim; Jong-Ho Lee; Chang-Ho Hyun, "A fire alarm vision system based on IR image processing," Information Science and Service Science (NISS), 2011 5th International Conference on New Trends in , vol.2, no., pp.291,293, 24-26 Oct. 2011
  6. Phillips, W., III; Shah, M.; Da Vitoria Lobo, N., "Flame recognition in video," Applications of Computer Vision, 2000, Fifth IEEE Workshop on. , vol., no., pp.224,229, 2000.
  7. Liqiang Wang; Mao Ye; Yuanxiang Zhu, "A hybrid fire detection using Hidden Markov Model and luminance map," Medical Image Analysis and Clinical Applications (MIACA), 2010 International Conference on , vol., no., pp.118,122, 10-13 June 2010.
  8. Budi, W.T.A.; Suwardi, I.S., "Fire alarm system based-on video processing," Electrical Engineering and Informatics (ICEEI), 2011 International Conference on , vol., no., pp.1,7, 17-19 July 2011.
  9. Yongquan Xia; Weili Li; Shaohui Ning, "Moving Object Detection Algorithm Based on Variance Analysis," Proceedings of International Workshop on Computer Science and Engineering, Oct. 2009.
  10. Fengliang Xu; Xia Liu; Fujimura, K., "Pedestrian detectionand tracking with night vision," IEEE Transactions on Intelligent Transportation Systems, vol.6, no.1, pp.63-71, March 2005 https://doi.org/10.1109/TITS.2004.838222
  11. 정수영, 김원호, "적외선 영상의 화염 검출을 위한 최적 문턱치 분석", 통신위성우주산업연구회논문지 제8권 제4호, pp.100-104, 2014