한국정보전자통신기술학회논문지 (The Journal of Korea Institute of Information, Electronics, and Communication Technology)

  • 제9권4호
  • /
  • Pages.343-350
  • /
  • 2016
  • /
  • 2005-081X(pISSN)
  • /
  • 2288-9302(eISSN)
한국정보전자통신기술학회 (Korea Information Electronic Communication Technology)
권호 표지
DOI QR코드

DOI QR Code

은닉마르코프모델과 DWT를 이용한 실시간 연기 검출

Realtime Smoke Detection using Hidden Markov Model and DWT

  • Kim, Hyung-O (Department of Electrical Engineering, SeoIl University)
  • 투고 : 2016.07.28
  • 심사 : 2016.08.25
  • 발행 : 2016.08.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 DWT에너지 기반의 연기 검출 방법을 제안하였다. 일반적으로 연기는 형태가 명확하지 않고 주변 환경에 의하여 색상, 형태, 확산방향 등의 특징이 가변적이기 때문에 특정 정보만을 이용할 경우에는 오검출율이 높아진다. 따라서 본 논문에서는 환경변화에 강인한 전경 추출 방법을 이용하여 객체를 검출하고 추출된 객체의 색상, 형태, DWT 에너지 정보를 통합적으로 사용하여 연기를 판단한다. 제안된 방법은 평균 30fps의 처리속도를 가지므로 실시간 처리가 가능하고 화재 발생 시점으로부터 연기 감지까지의 평균 소요시간이 약 7초로 빠른 조기감지가 가능하며 낮은 오검출율을 나타내었다.

In this paper, We proposed a realtime smoke detection using hidden markov model and DWT. The smoke type is not clear. The color of the smoke, form, spread direction, etc., are characterized by varying the environment. Therefore, smoke detection using specific information has a high error rate detection. Dynamic Object Detection was used a robust foreground extraction method to environmental changes. Smoke recognition is used to integrate the color, shape, DWT energy information of the detected object. The proposed method is a real-time processing by having the average processing speed of 30fps. The average detection time is about 7 seconds, it is possible to detect early rapid.

키워드

참고문헌

  1. Rubaiyat Yasmin, "Detection of Smoke Propagation Direction Using Color Video Sequenxes," International Journal of Soft Computing 4(1), pp. 45-48, 2009
  2. Chao-Ching Ho, Tzu-Hsin Kuo, "Real-time video-based fire smoke detection system," Advanced Intelligent Mechatronics IEEE/ASME International Coference, pp.1845-1850, July 2009
  3. Nobuyuki Fujiwara, Kenji Terada, "Extraction of a Smoke Region Using Fractal Coding," International Symposium on Communications and Information Technologies, Sapporo, Japan, pp.659-662, October 2004
  4. F Gomez-Rodriguez, "Smoke Monitoring and measurement Using Image processing. Application to Forest Fires," Automatic Target Recognition XIII, Proceedings of SPIE Vol.5094, pp.404-411, 2003
  5. Turgay Celik, Huseyin uzkaramanli, Hasan Demirel, "FIRE AND SMOKE DETECTION WITHOUT SENSORS : IMAGE PROCESSING BASED APPROACH," EUSIPCO 2007, pp.3-7, 2007
  6. B. U Toreyin, Yi githan Dedeoglu, A. Enis Cetin, "Wavelet based real-time smoke detection in video," Signal Processing: Image. Communication, EURASIP, Elsevier, vol. 20, pp. 255-258, 2005
  7. B. U Toreyin, Yi githan Dedeoglu, A. Enis Cetin, "Contour based smoke detection in video using wavelets," 14th European Signal Processing Conference EUSIPCO, pp. 1-5, 2006
  8. Stauffer, C. Grimson, W.E.L., "Adaptive background mixture models for real-time tracking," Computer Vision and Pattern Recognition IEEE Computer Society Conference , vol. 2, June 1999
  9. Chui, Charles K, "An Introduction to Wavelets," San Diego: Academic Press, 1992
  10. Yuan Wei, Yu Chunyu, Zhang Yongming , "Based on wavelet transformation fire smoke detection method," Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on, pp. 2-872-285, August 2009
  11. http://en.wikipedia.org/wiki/Connected_Component_Labeling
  12. http://en.wikipedia.org/wiki/YUV
  13. Lloyd R. Welch, "Hidden Markov Models and the Baum-Welch Algorithm," IEEE Information Theory Society Newsletter, vol. 53, no.4, December 2003
  14. B. U Toreyin, Yi githan Dedeoglu, A. Enis Cetin, "FLAME DETECTION IN VIDEO USING HIDDEN MARKOV MODELS," ICIP 2005, pp. II-1230-1233, 2005