Fire Detection Algorithms Using Multimodal ... - Bilkent University

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CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 9There are several video-based fire and flame detection algorithms in the literature[64], [17], [48], [78], [38], [81], [80], [94]. These methods make use ofvarious visual signatures including color, motion and geometry of fire regions.Healey et al. [38] use only color clues for flame detection. Phillips et al. [64]use pixel colors and their temporal variations. Chen et al. [17] utilize a changedetection scheme to detect flicker in fire regions. In [78], Fast Fourier Transforms(FFT) of temporal object boundary pixels are computed to detect peaksin Fourier domain, because it is claimed that turbulent flames flicker with a characteristicflicker frequency of around 10 Hz independent of the burning materialand the burner in a mechanical engineering paper [1], [14]. We observe that flameflicker process is a wide-band activity below 12.5 Hz in frequency domain for apixel at the boundary of a flame region in a color-video clip recorded at 25 fps(cf. Fig. 2.1). Liu and Ahuja [48] also represent the shapes of fire regions inFourier domain. However, an important weakness of Fourier domain methods isthat flame flicker is not purely sinusoidal but it’s random in nature. Therefore,there may not be any peaks in FFT plots of fire regions. In addition, FourierTransform does not have any time information. Therefore, Short-Time FourierTransform (STFT) can be used requiring a temporal analysis window. In thiscase, temporal window size becomes an important parameter for detection. Ifthe window size is too long, one may not observe peakiness in the FFT data. Ifit is too short, one may completely miss cycles and therefore no peaks can beobserved in the Fourier domain.Our method not only detects fire and flame colored moving regions in video butalso analyzes the motion of such regions in wavelet domain for flicker estimation.The appearance of an object whose contours, chrominance or luminosity valuesoscillate at a frequency higher than 0.5 Hz in video is an important sign of thepossible presence of flames in the monitored area [78].High-frequency analysis of moving pixels is carried out in wavelet domain inour work. There is an analogy between the proposed wavelet domain motion analysisand the temporal templates of [21] and the motion recurrence images of [43],which are ad hoc tools used by computer scientists to analyze dancing peopleand periodically moving objects and body parts. However, temporal templates
CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 10600Flicker Frequency Distribution − 25 fps500400Abs. DFT coef.3002001000−15 −10 −5 0 5 10 15Frequency (Hz)Figure 2.1: Flicker frequency distribution for a pixel at the boundary of a flameregion in a color-video clip recorded at 25 fps. This frequency distribution isobtained by analyzing the temporal variations in the red channel value of thepixel.
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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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