Fire Detection Algorithms Using Multimodal ... - Bilkent University

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CONTENTS xii 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6 Wildfire Detection 81 6.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.2 Building Blocks of Wildfire Detection Algorithm . . . . . . . . . . 84 6.2.1 Detection of Slow Moving Objects . . . . . . . . . . . . . . 85 6.2.2 Detection of Smoke-Colored Regions . . . . . . . . . . . . 86 6.2.3 Detection of Rising Regions . . . . . . . . . . . . . . . . . 87 6.2.4 Shadow Detection and Removal . . . . . . . . . . . . . . . 89 6.3 Adaptation of Sub-algorithm Weights . . . . . . . . . . . . . . . . 91 6.3.1 Set Theoretic Analysis of the Weight Update Algorithm . 95 6.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 7 Conclusion and Future Work 106 Bibliography 110
List of Figures 2.1 Flicker frequency distribution for a pixel at the boundary of a flame region in a color-video clip recorded at 25 fps. This frequency distribution is obtained by analyzing the temporal variations in the red channel value of the pixel. . . . . . . . . . . . . . . . . . . 10 2.2 (a) A sample flame pixel process in RGB space, and (b) the spheres centered at the means of the Gaussian distributions with radius twice the standard deviation. . . . . . . . . . . . . . . . . . . . . 15 2.3 A two-stage filter bank. HPF and LPF represent half-band highpass and low-pass filters, with filter coefficients {− 1 4 , 1 2 , − 1 4 } and { 1, 1, 1 }, respectively. This filter bank is used for wavelet analysis. 18 4 2 4 2.4 (a) Temporal variation of image pixel at location x = (111, 34), Ĩ n (x). The pixel at x = (111, 34) is part of a flame for image frames I(x, n), n=1, 2, 3, 19, 23, 24, 41 and 50. It becomes part of the background for n = 12,..., 17, 20, 21, 26, 27, 31,..., 39, 45, 52,..., and 60. Wavelet domain subsignals (b) d n and (c) e n reveal the fluctuations of the pixel at location x = (111, 34). . . . . . . 20 2.5 (a) Temporal history of the pixel at location x = (18, 34). It is part of a fire-colored object for n = 4, 5, 6, 7, and 8, and it becomes part of the background afterwards. Corresponding subsignals (b) d n and (c) e n exhibit stationary behavior for n > 8. . . . . . . . . 21 xiii
Page 1 and 2: FIRE DETECTION ALGORITHMS USING MUL
Page 3 and 4: I certify that I have read this the
Page 5 and 6: v Signal processing techniques for
Page 7 and 8: vii için de işaret işleme yönte
Page 9 and 10: ix This work was supported in part
Page 11: CONTENTS xi 2.4 Experimental Result
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Page 19 and 20: LIST OF TABLES xix 4.1 Detection re
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Chapter 4 Short Range Smoke Detecti
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Chapter 5 Flame Detection Using PIR
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CHAPTER 5. FLAME DETECTION USING PI
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CHAPTER 6. WILDFIRE DETECTION 82 In
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CHAPTER 6. WILDFIRE DETECTION 86 D
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CHAPTER 6. WILDFIRE DETECTION 90 Th
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Chapter 7 Conclusion and Future Wor
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CHAPTER 7. CONCLUSION AND FUTURE WO
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Bibliography [1] B. Albers and A. A
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BIBLIOGRAPHY 112 [18] D. Chetveriko
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BIBLIOGRAPHY 114 [38] G. Healey, D.
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BIBLIOGRAPHY 116 [56] U. Niesen, D.
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BIBLIOGRAPHY 118 [77] C. Stauffer a
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