Fire Detection Algorithms Using Multimodal ... - Bilkent University

More documents

Recommendations

Info

CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 21Figure 2.5: (a) Temporal history of the pixel at location x = (18, 34). It ispart of a fire-colored object for n = 4, 5, 6, 7, and 8, and it becomes part ofthe background afterwards. Corresponding subsignals (b) d n and (c) e n exhibitstationary behavior for n > 8.
CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 22and 8, then it becomes part of the background again for n > 8. Correspondingwavelet signals d n and e n do not exhibit oscillatory behavior as shown in Fig. 2.5.Small variations due to noise around zero after the 10 − th frame are ignored bysetting up a threshold.The number of wavelet stages needed for used in flame flicker analysis isdetermined by the video capture rate. In the first stage of dyadic wavelet decomposition,the low-band subsignal and the high-band wavelet subsignal d n (x)of the signal Ĩ n (x) are obtained. The subsignal d n (x) contains [2.5 Hz, 5 Hz]frequency band information of the original signal Ĩ n (x) in 10 Hz video framerate. In the second stage the low-band subsignal is processed once again using adyadic filter bank, and the wavelet subsignal e n (x) covering the frequency band[1.25 Hz, 2.5 Hz] is obtained. Thus by monitoring wavelet subsignals e n (x) andd n (x), one can detect fluctuations between 1.25 to 5 Hz in the pixel at location x.Flame flicker can be detected in low-rate image sequences obtained with a rateof less than 20 Hz as well in spite of the aliasing phenomenon. To capture 10 Hzflicker, the video should capture at least 20 frames per second (fps). However, insome surveillance systems, the video capture rate is below 20 Hz. If the video isavailable at a lower capture rate, aliasing occurs but flicker due to flames can stillbe observed in the video. For example, 8 Hz sinusoid appears as 2 Hz sinusoid in a10 fps video [87]. Aliased version of flame flicker signal is also a wide-band signalin discrete-time Fourier Transform domain. This characteristic flicker behavior isvery well suited to be modeled as a random Markov model which is extensivelyused in speech recognition systems and recently these models have been used incomputer vision applications [6].Three-state Markov models are trained off-line for both flame and non-flamepixels to represent the temporal behavior (cf. Fig.4.3). These models are trainedby using first-level wavelet coefficients d n (x) corresponding to the intensity valuesĨ n (x) of the flame-colored moving pixel at location x as the feature signal. Asingle-level decomposition of the intensity variation signal Ĩn(x) is sufficient tocharacterize the turbulent nature of flame flicker. One may use higher-orderwavelet coefficients such as, e n (x), for flicker characterization, as well. However,
Page 3 and 4: I certify that I have read this the
Page 7 and 8: viiiçin de işaret işleme yöntem
Page 9: ixThis work was supported in part b
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 39: CHAPTER 2. FLAME DETECTION IN VISIB
Page 55 and 56: 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 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:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
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
Page 113 and 114:
CHAPTER 6. WILDFIRE DETECTION 94as
Page 115 and 116:
CHAPTER 6. WILDFIRE DETECTION 96Sol
Page 117 and 118:
CHAPTER 6. WILDFIRE DETECTION 98as
Page 119 and 120:
CHAPTER 6. WILDFIRE DETECTION 100Un
Page 121 and 122:
CHAPTER 6. WILDFIRE DETECTION 102is
Page 123 and 124:
CHAPTER 6. WILDFIRE DETECTION 104Ta
Page 125 and 126:
Chapter 7Conclusion and Future Work
Page 127 and 128:
CHAPTER 7. CONCLUSION AND FUTURE WO
Page 129 and 130:
Bibliography[1] B. Albers and A. Ag
Page 131 and 132:
BIBLIOGRAPHY 112[18] D. Chetverikov
Page 133 and 134:
BIBLIOGRAPHY 114[38] G. Healey, D.
Page 135 and 136:
BIBLIOGRAPHY 116[56] U. Niesen, D.
Page 137 and 138:
BIBLIOGRAPHY 118[77] C. Stauffer an
show all

Fire Detection Algorithms Using Multimodal ... - Bilkent University

Create successful ePaper yourself

Delete template?

Save as template?