Fire Detection Algorithms Using Multimodal ... - Bilkent University

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LIST OF FIGURESxvi4.4 Two moving objects in video: smoke image (top), and a vehicle(bottom). The object boundaries are determined by the backgroundsubtraction algorithm. . . . . . . . . . . . . . . . . . . . . 614.5 Equally spaced 64 contour points of smoke (top) and the vehicleregions (bottom) shown in Fig.4.4. . . . . . . . . . . . . . . . . . 624.6 The absolute a) wavelet and b) low-band coefficients for the smokeregion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.7 The absolute a) wavelet and b) low-band coefficients for the vehicle. 645.1 The circuit diagram for capturing an analog signal output from aPIR sensor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705.2 Flame flicker spectrum distribution. PIR signal is sampled with50 Hz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715.3 A typical PIR sensor output sampled at 50 Hz with 8 bit quantizationwhen there is no activity within its viewing range. . . . . . 725.4 PIR sensor output signals recorded at a distance of 5m for a (a)walking person, and (b) flame. . . . . . . . . . . . . . . . . . . . . 735.5 Two three-state Markov models are used to represent (a) ‘fire’ and(b) ‘non-fire’ classes, respectively. . . . . . . . . . . . . . . . . . . 755.6 A typical plot of the dissimilarity function D(T1,T2)x10 −4 . It ismulti-modal and non-differentiable. . . . . . . . . . . . . . . . . . 775.7 The PIR sensor is encircled. The fire is close to die out completely.A man is also within the viewing range of the sensor. . . . . . . . 806.1 Snapshot of a typical wildfire smoke captured by a forest watchtower which is 5 km away from the fire (rising smoke is markedwith an arrow). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
LIST OF FIGURESxvii6.2 Markov model λ 1 corresponding to wildfire smoke (left) and theMarkov model λ 2 of clouds (right). Transition probabilities a ijand b ij are estimated off-line. . . . . . . . . . . . . . . . . . . . . 886.3 Orthogonal Projection: Find the vector w(n+1) on the hyperplaney(x, n) = D T (x, n)w minimizing the distance between w(n) andthe hyperplane. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956.4 Geometric interpretation: Weight vectors corresponding to decisionfunctions at each frame are updated as to satisfy the hyperplaneequations defined by the oracle’s decision y(x, n) and thedecision vector D(x, n). Lines in the figure represent hyperplanesin R M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 976.5 The pseudo-code for the active decision fusion algorithm . . . . . 996.6 The pseudo-code for the universal predictor . . . . . . . . . . . . 1006.7 The pseudo-code for the Weighted Majority Algorithm . . . . . . 1016.8 Sequence of frames excerpted from clip V 15. Within 18 secondsof time, cloud shadows cover forestal area which results in a falsealarm in an untrained algorithm with decision weights equal to14depicted as a bounding box. The proposed algorithm does notproduce a false alarm in this video. . . . . . . . . . . . . . . . . . 1036.9 The average error curves for the LMS, universal predictor, andthe WMA based adaptation algorithms in clip V 13.All of thealgorithms converge to the same average error value in this case,however the convergence rate of the LMS algorithm is faster thanboth the universal predictor and the WMA algorithm. . . . . . . . 105
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 18 and 19: List of Tables1.1 Organization of t
Page 20 and 21: Chapter 1IntroductionDynamic textur
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CHAPTER 3. FLAME DETECTION IN INFRA
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Chapter 4Short Range Smoke Detectio
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Chapter 5Flame Detection Using PIRS
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CHAPTER 5. FLAME DETECTION USING PI
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Chapter 7Conclusion and Future Work
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CHAPTER 7. CONCLUSION AND FUTURE WO
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Bibliography[1] B. Albers and A. Ag
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BIBLIOGRAPHY 112[18] D. Chetverikov
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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 an
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