한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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RGB-Depth 카메라 기반의 실내 연기검출

Smoke Detection Based on RGB-Depth Camera in Interior

  • 박장식 (경성대학교 전자공학과학과)
  • 투고 : 2013.10.18
  • 심사 : 2014.02.11
  • 발행 : 2014.02.28
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초록

본 논문에서 RGB-Depth 카메라를 이용하여 실내에서의 연기를 검출하는 알고리즘을 제안한다. RGB-Depth 카메라는 RGB 색영상과 깊이 정보를 제공한다. 키넥트(Kinect)는 특정한 패턴의 적외선을 출력하고 이를 적외선 카메라로 수집하고 분석하여 깊이 정보를 획득한다. 특정한 패턴을 구성하는 점들 각각에 대하여 거리를 측정하고 객체면의 깊이를 추정한다. 따라서, 이웃하는 점들의 깊이 변화가 많은 객체인 경우에는 객체면의 깊이를 결정하지 못한다. 연기의 농도가 일정 주파수로 변화하고, 적외선 영상의 이웃하는 화소간의 변화가 많기 때문에 키넥트가 깊이를 결정하지 못한다. 본 논문에서는 연기에 대한 키넥트의 특성을 이용하여 연기를 검출한다. 키넥트가 깊이를 결정하지 못한 영역을 후보영역으로 설정하고, 색영상의 밝기가 임계값보다 큰 경우 연기영역으로 결정한다. 본 논문에서는 시뮬레이션을 통하여 실내에서의 연기 검출에 RGB-Depth 카메라가 효과적임을 확인할 수 있다.

In this paper, an algorithm using RGB-depth camera is proposed to detect smoke in interrior. RGB-depth camera, the Kinect provides RGB color image and depth information. The Kinect sensor consists of an infra-red laser emitter, infra-red camera and an RGB camera. A specific pattern of speckles radiated from the laser source is projected onto the scene. This pattern is captured by the infra-red camera and is analyzed to get depth information. The distance of each speckle of the specific pattern is measured and the depth of object is estimated. As the depth of object is highly changed, the depth of object plain can not be determined by the Kinect. The depth of smoke can not be determined too because the density of smoke is changed with constant frequency and intensity of infra-red image is varied between each pixels. In this paper, a smoke detection algorithm using characteristics of the Kinect is proposed. The region that the depth information is not determined sets the candidate region of smoke. If the intensity of the candidate region of color image is larger than a threshold, the region is confirmed as smoke region. As results of simulations, it is shown that the proposed method is effective to detect smoke in interior.

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참고문헌

  1. X. Zhou, F. Yu, Y. Wen, Z. Lu, and G. Song, "Early Fire Detection Based on Flame Contours in Video," In Proc. on Information Technology J., 2010, pp. 1-10.
  2. S. Surit and W. Chatwiriya, "Forest Fire Smoke Detection in Video Based on Digital Image Processing Approach with Static and Dynamic Characteristric Analysis," In Proc. on Int. Conf. on Computers, Networks, Systems and Industrial Engineering, 2011, pp. 35-39.
  3. J. Park, H. Kim, and Y. Yu, "Video Based Fire Detection Algorithm Using Gaussian Mixture Model," J. of The Korea Institute of Electronic Communication Sciences, vol. 6, no. 2, 2011, pp. 206-211.
  4. Y. Dedeoglu, B. U. Toreyin, U. Gudukbay, and A. E. Cetin, "Real-time Fire and Flame Detection in Video," In Proc. Acoustics, Speech and Signal Processing, vol. 2, March 18-23, 2005, pp. 669-672.
  5. B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, "Flame Detection in Video Using Hidden Markov Models," In Proc. Int. Conf. Image Processing, vol. 2, Sept. 11-14, 2005, pp. 1230-1233.
  6. Z. Teng, J. Kim, and D. Kang, "Fire Detection Based on Hidden Markov Models," Int. J. of Control, Automation and Systems, vol. 8, no. 4, 2010, pp. 822-830. https://doi.org/10.1077/s12555-010-0414-2
  7. J.-S. Park, J.-K. Song, and B.-W. Yoon, "Gaussian Mixture Model Based Smoke Detection Algorithm Robust to Lights Variations," J. of The Korea Institute of Electronic Communication Sciences, vol. 7, no. 4, 2012, pp. 733-739.
  8. B. Freedman, "Method and System for Object Reconstrucion," US Patent US 2010/0118123A1
  9. K. Khoshelham and S. O. Elberink, "Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Application," Sensors, 2012, pp. 1437-1454.
  10. K. Khoshelham, "Accuracy Analysis of Kinect Depth Data," Geo Information Science 38(5/W12), 2010, pp. 1-6.