Fire Detection Algorithms Using Multimodal ... - Bilkent University

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CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 37The proposed algorithm is designed for ranges up to 30 meters. After thisrange, flame flicker is hard to observe and object contours cannot be estimatedaccurately.3.2 <strong>Fire</strong> and Flame Behavior in IR VideoMost IR imaging sensors provide a measure of the heat distribution in the scenein a range of wavelengths and each pixel has a single value. Usually, hot (cold)objects in the scene are displayed as bright (dark) regions in white-hot mode inIR cameras. Therefore fire and flame pixels appear as local maxima in an IRimage. If a relatively bright region moves in the captured video then it shouldbe marked as a potential region of fire in the scene monitored by the IR camera.However, an algorithm based on only motion and brightness information willproduce many false alarms because vehicles, animals, and people are warmer thanthe background and they also appear as bright objects. In the proposed approach,in addition to motion and relative brightness information object boundaries areanalyzed both spatially (intra-frame) and temporally (inter-frame).Boundaries of uncontrolled fire regions in an image frame are obviously irregular.On the other hand, almost all regular objects and people have smoothand stationary boundaries. This information is modeled using wavelet domainanalysis of moving object contours which is described in the next subsection. Onecan reduce the false alarms which may be due to ordinary moving hot objectsby carrying out temporal analysis around object boundaries to detect randomchanges in object contours. This analysis is described in Section 3.2.2.3.2.1 Wavelet Domain Analysis of Object ContoursMoving objects in IR video are detected using the background estimation methoddeveloped by Collins et al. [19]. This method assumes that the camera is stationary.Moving pixels are determined by subtracting the current image from the
CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 38background image and thresholding. A recursive adaptive threshold estimationis described in [19] as well. Other methods can be also used for moving objectestimation. After moving object detection, it is checked whether the object ishotter than the background, i.e., it is verified if some of the object pixels arehigher in value than the background pixels.Hot objects and regions in IR video can be determined in moving cameras aswell by estimating local maxima in the image. Contours of these high temperatureregions can be determined by region growing.The next step of the proposed method is to determine the center of massof the moving bright object.A one-dimensional (1-D) signal x(θ) is obtainedby computing the distance from the center of mass of the object to the objectboundary for 0 ≤ θ < 2π. In Fig. 3.1, two FLIR (forward looking infra-red) imageframes are shown. Example feature functions for walking man pointed with anarrow and the fire region in Fig. 3.1 are shown in Fig. 4.5 for 64 equally spacedangles x[l] = x(lθ s ), θ s = 2π . To determine the high-frequency content of a curve,64we use a single scale wavelet transform shown in Fig. 4.2. The feature signal x[l]is fed to a filterbank shown in Fig. 4.2 and the low-band signalc[l] = ∑ mh[2l − m]x[m] (3.1)and the high-band subsignalw[l] = ∑ mg[2l − m]x[m] (3.2)are obtained. Coefficients of the lowpass and the highpass filters are h[l] ={ 1, 1, 1} and g[l] = {− 1, 1, − 1 }, respectively [34], [11], [45].4 2 4 4 2 4The absolute values of high-band (wavelet) w[l] and low-band c[l] coefficientsof the fire region and the walking man are shown in Figs. 4.6 and 4.7, respectively.The high-frequency variations of the feature signal of the fire region isclearly distinct from that of the man. Since regular objects have relatively smoothboundaries compared to flames, the high-frequency wavelet coefficients of flameboundary feature signals have more energy than regular objects. Therefore, the
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I certify that I have read this the
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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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