Fire Detection Algorithms Using Multimodal ... - Bilkent University
Fire Detection Algorithms Using Multimodal ... - Bilkent University Fire Detection Algorithms Using Multimodal ... - Bilkent University
CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 43regions to reach a final decision.next subsection.Flicker detection process is described in the3.2.2 Modeling Temporal Flame BehaviorIt was reported in mechanical engineering literature that turbulent flames flickerwith a frequency of 10 Hz [1]. In [48], the shape of fire regions are represented inFourier domain. Since, Fourier Transform does not carry any time information,FFTs have to be computed in windows of data and temporal window size and thepeak or energy around 10 Hz is very critical for flicker detection. If the windowsize is too long then one may not get enough peaks in the FFT data. If it istoo short then one may completely miss flicker and therefore no peaks can beobserved in the Fourier domain. Furthermore, one may not observe a peak at10 Hz but a plateau around it, which may be hard to distinguish from the Fourierdomain background.Another problem is that one may not detect periodicity in fast growing uncontrolledfires because the boundary of fire region simply grows in video. Actually,the fire behavior is a wide-band random activity below 15 Hz and a random processbased modeling approach is naturally suited to characterize the rapid timevaryingcharacteristic of flame boundaries. Broadbent [5] and Huang et al. [42]independently reported different flicker frequency distributions for various fueltypes. In general, a pixel especially at the edge of a flame becomes part of thefire and disappears in the background several times in one second of a video atrandom. In fact, we analyzed the temporal characteristics of the red channelvalue of a pixel at the boundary of a flame region in color-video clips recorded at10 fps and 25 fps. We also analyzed the temporal characteristics of the intensityvalue of a pixel at the boundary of a flame region in an IR video clip recordedat 10 fps. We obtained the flicker frequency distributions shown in Fig. 3.6 for10 fps color video, 25 fps color video and 10 fps IR video, respectively. We assumedthat the flame flicker behavior is a wide-band random activity below 15 Hzfor all practical purposes. This is the basic reason behind our stochastic model.
CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 44Flicker Frequency Distribution − 10 fpsAbs. DFT coef.Abs. DFT coef.Abs. DFT coef.100050010000−5 −4 −3 −2 −1 0 1 2 3 4 5Flicker Frequency Distribution − 25 fpsFrequency (Hz)5000−12.5 −10 −5 0 5 10 12.5Flicker Frequency Distribution − 10 fps/IRFrequency (Hz)10005000−5 −4 −3 −2 −1 0 1 2 3 4 5Frequency (Hz)Figure 3.6: Flicker frequency distributions for a) 10 fps color video, b) 25 fpscolor video and c) 10 fps IR video. These frequency distributions were obtainedby analyzing the temporal variations in the red channel value of a pixel at theboundary of a flame region in color-video clips recorded at 10 fps and 25 fps andintensity value of a pixel at the boundary of a flame region in an IR video cliprecorded at 10 fps, respectively.
- Page 14 and 15: LIST OF FIGURESxiv2.6 Three-state M
- Page 16 and 17: LIST OF FIGURESxvi4.4 Two moving ob
- Page 18 and 19: List of Tables1.1 Organization of t
- Page 20 and 21: Chapter 1IntroductionDynamic textur
- Page 22: CHAPTER 1. INTRODUCTION 3No fire de
- Page 27 and 28: Chapter 2Flame Detection in Visible
- Page 29 and 30: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 31 and 32: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 33 and 34: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 35 and 36: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 37 and 38: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 39 and 40: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 41 and 42: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 43 and 44: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 45 and 46: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 47 and 48: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 49 and 50: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 51 and 52: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 53 and 54: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 55 and 56: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 57 and 58: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 59 and 60: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 61: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 65 and 66: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 67 and 68: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 69 and 70: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 71 and 72: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 73 and 74: Chapter 4Short Range Smoke Detectio
- Page 75 and 76: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 77 and 78: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 79 and 80: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 81 and 82: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 83 and 84: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 85 and 86: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 87 and 88: Chapter 5Flame Detection Using PIRS
- Page 89 and 90: CHAPTER 5. FLAME DETECTION USING PI
- Page 91 and 92: CHAPTER 5. FLAME DETECTION USING PI
- Page 93 and 94: CHAPTER 5. FLAME DETECTION USING PI
- Page 95 and 96: CHAPTER 5. FLAME DETECTION USING PI
- Page 97 and 98: CHAPTER 5. FLAME DETECTION USING PI
- Page 99 and 100: CHAPTER 5. FLAME DETECTION USING PI
- Page 101 and 102: CHAPTER 6. WILDFIRE DETECTION 82In
- Page 103 and 104: CHAPTER 6. WILDFIRE DETECTION 84are
- Page 105 and 106: CHAPTER 6. WILDFIRE DETECTION 86D 1
- Page 107 and 108: CHAPTER 6. WILDFIRE DETECTION 88tha
- Page 109 and 110: CHAPTER 6. WILDFIRE DETECTION 90The
- Page 111 and 112: CHAPTER 6. WILDFIRE DETECTION 92D 1
CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 44Flicker Frequency Distribution − 10 fpsAbs. DFT coef.Abs. DFT coef.Abs. DFT coef.100050010000−5 −4 −3 −2 −1 0 1 2 3 4 5Flicker Frequency Distribution − 25 fpsFrequency (Hz)5000−12.5 −10 −5 0 5 10 12.5Flicker Frequency Distribution − 10 fps/IRFrequency (Hz)10005000−5 −4 −3 −2 −1 0 1 2 3 4 5Frequency (Hz)Figure 3.6: Flicker frequency distributions for a) 10 fps color video, b) 25 fpscolor video and c) 10 fps IR video. These frequency distributions were obtainedby analyzing the temporal variations in the red channel value of a pixel at theboundary of a flame region in color-video clips recorded at 10 fps and 25 fps andintensity value of a pixel at the boundary of a flame region in an IR video cliprecorded at 10 fps, respectively.