Smoke Detection Method of Color Image Using Object Block Ternary Pattern

물체 블록의 삼진 패턴을 이용한 컬러 영상의 연기 검출 방법

  • 이용훈 (공주대학교 전기전자제어공학부) ;
  • 김원호 (공주대학교 전기전자제어공학부)
  • Received : 2014.09.29
  • Accepted : 2014.10.30
  • Published : 2014.12.31

Abstract

Color image processing based on smoke detection is suitable detecting target to early detection of fire smoke. A method for detecting the smoke is processed in the pre-processing movement and color. And Next, characteristics of smoke such as diffusion, texture, shape, and directionality are used to post-processing. In this paper, propose the detection method of density distribution characteristic in characteristics of smoke. the generate a candidate regions by color thresholding image in Detecting the movement of smoke to the 10Frame interval and accumulated while 1second image. then check whether the pattern of the smoke by candidate regions to applying OBTP(Object Block Ternary Pattern). every processing is Block-based processing, moving detection is decided the candidate regions of the moving object by applying an adaptive threshold to frame difference image. The decided candidate region accumulates one second and apply the threshold condition of the smoke color. make the ternary pattern compare the center block value with block value of 16 position in each candidate region of the smoke, and determine the smoke by compare the candidate ternary pattern and smoke ternary pattern.

컬러 영상 처리 기반의 연기 검출은 화재의 조기 검출에 적합한 검출 대상이다. 연기 검출을 위한 방법으로 움직임과 색상이 전처리로서 처리되며, 확산, 질감, 형태, 방향성 등의 성질이 후처리로서 사용된다. 본 논문은 연기의 특성 중 밀도적인 분포 특성 검출 방법을 제안한다. 연기의 움직임을 10Frame 간격으로 1초 동안 축적한 이미지에 색상을 문턱치 처리해 후보영역을 생성하고, OBTP(Object Block Ternary Pattern)을 적용해 연기의 패턴임을 확인한다. 모든 처리는 Block 기반으로, 움직임 검출은 차분 영상에 적응 문턱치를 적용해 움직이는 물체의 후보영역을 결정했다. 결정된 후보영역을 1초간 축적하고 연기 색상의 문턱치 조건을 적용한다. 각각의 연기 후보 영역을 특정 위치의 16개 Block 값을 중앙 Block 값과 비교하고 삼진화 된 패턴을 연기의 패턴과 비교하여 연기를 결정한다.

Keywords

References

  1. Yongquan Xia, Weili Li,Shaohui Ning,"Moving Object Detection Alogorithm Based on Variance Analysis"Computer Science and Engineering, p.347-350, Volume.1, 28-30 Oct. 2009, Qingdao
  2. TH Chen, YH Yin, SF Huang,"The Smoke Detection for Early Fire-Alarming System Base on Video Processing" Intelligent Information Hiding and Multimedia Signal Processing, 2006
  3. Yue Wang, Teck Wee Chua, Richard Chang and Nam Trung Pham,"Real-Time Smoke Detection Using Texture and Color Features",Pattern Recognition (ICPR), 2012
  4. DongKeun Kim, Yuan-Fang Wang2, "Smoke Detection In Video",Computer Science Information Engineering, 2009
  5. S. Lin and D. J. "Improvement of a Video Smoke Detection Based on Accumulative Motion Orientation Model", Electronics, Robotics and Automotive Mechanics Conference (CERMA), p.126-130, 15-18, Nov. 2011
  6. Zheng Wei, Xingang Wang, Wenchuan An, Jianfeng Che,"Target-Tracking Based Early Fire Smoke Detection in Video" Image and Graphics, p.172-176, 20-23 Sept. 2009
  7. Hidenori Maruta, Fujio Kurokawa, Yusuke lida, "Image Based Smoke Detection with Two- Dimensional Local Hurst Exponent"Industrial Electronics(ISIE), p.1651-1656 ,4-7 July. 2010
  8. BU Treyin, Y Dedeoglu, AE Cetin "Wavelet Based Real-Time Smoke Detection In Video" 13th European Signal Processing Conference EUSIPCO 2005, Antalya
  9. 이용훈, 김원호, "화재 영상감시를 위한 표준 색상모델의 연기색상 분석", 한국산학기술학회논문지, Vol.14, NO.9, p.4472-4477, 2013.
  10. Hidenori Maruta, Fujio Kurokawa, Yusuke lida, "Anisotropic LBP descriptors for robust smoke detection", Industrial Electronics Society, IECON $39^{th}$ Annual Conferecne of the IEEE, 10-13, Nov. 2013, vienna