Fire Detection Algorithms Using Multimodal ... - Bilkent University

Fire Detection Algorithms Using Multimodal ... - Bilkent University Fire Detection Algorithms Using Multimodal ... - Bilkent University

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CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 25the flame and non-flame pixel models. The model yielding higher probability isdetermined as the result of the analysis for each of the candidate pixel. A pixelis called as a flame or a non-flame pixel according to the result of this analysis.A fire mask composing of flame pixels is formed as the output of the method.The probability of a Markov model producing a given sequence of wavelet coefficientsis determined by the sequence of state transition probabilities. Therefore,the flame decision process is insensitive to the choice of thresholds T 1 and T 2 ,which basically determine if a given wavelet coefficient is close to zero or not.Still, thresholds can be determined using a k-means type algorithm, as well.2.2.4 Spatial Wavelet AnalysisThe fourth step of our fire detection algorithm is the spatial wavelet analysis ofmoving regions containing Fire mask pixels to capture color variations in pixelvalues. In an ordinary fire-colored object there will be little spatial variations inthe moving region as shown in Fig. 2.7 (a). On the other hand, there will besignificant spatial variations in a fire region as shown in Fig. 2.8 (a). The spatialwavelet analysis of a rectangular frame containing the pixels of fire-colored movingregions is performed. The images in Figs. 2.7 (b) and 2.8 (b) are obtained after asingle stage two-dimensional wavelet transform that is implemented in a separablemanner using the same filters explained in Subsection 2.2.3. Absolute values oflow-high, high-low and high-high wavelet subimages are added to obtain theseimages. A decision parameter v 4 is defined for this step, according to the energyof the wavelet subimages:v 4 =1M × N∑|I lh (k, l)| + |I hl (k, l)| + |I hh (k, l)|, (2.12)k,lwhere I lh (k, l) is the low-high subimage, I hl (k, l) is the high-low subimage, andI hh (k, l) is the high-high subimage of the wavelet transform, respectively, andM × N is the number of pixels in the fire-colored moving region. If the decisionparameter of the fourth step of the algorithm, v 4 , exceeds a threshold, then it islikely that this moving and fire-colored region under investigation is a fire region.

CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 26(a)(b)Figure 2.7: (a) A child with a fire-colored t-shirt, and b) the absolute sum of spatialwavelet transform coefficients, |I lh (k, l)|+|I hl (k, l)|+|I hh (k, l)|, of the regionbounded by the indicated rectangle.(a)(b)Figure 2.8: (a) Fire, and (b) the absolute sum of spatial wavelet transform coefficients,|I lh (k, l)|+|I hl (k, l)|+|I hh (k, l)|, of the region bounded by the indicatedrectangle.

CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 25the flame and non-flame pixel models. The model yielding higher probability isdetermined as the result of the analysis for each of the candidate pixel. A pixelis called as a flame or a non-flame pixel according to the result of this analysis.A fire mask composing of flame pixels is formed as the output of the method.The probability of a Markov model producing a given sequence of wavelet coefficientsis determined by the sequence of state transition probabilities. Therefore,the flame decision process is insensitive to the choice of thresholds T 1 and T 2 ,which basically determine if a given wavelet coefficient is close to zero or not.Still, thresholds can be determined using a k-means type algorithm, as well.2.2.4 Spatial Wavelet AnalysisThe fourth step of our fire detection algorithm is the spatial wavelet analysis ofmoving regions containing <strong>Fire</strong> mask pixels to capture color variations in pixelvalues. In an ordinary fire-colored object there will be little spatial variations inthe moving region as shown in Fig. 2.7 (a). On the other hand, there will besignificant spatial variations in a fire region as shown in Fig. 2.8 (a). The spatialwavelet analysis of a rectangular frame containing the pixels of fire-colored movingregions is performed. The images in Figs. 2.7 (b) and 2.8 (b) are obtained after asingle stage two-dimensional wavelet transform that is implemented in a separablemanner using the same filters explained in Subsection 2.2.3. Absolute values oflow-high, high-low and high-high wavelet subimages are added to obtain theseimages. A decision parameter v 4 is defined for this step, according to the energyof the wavelet subimages:v 4 =1M × N∑|I lh (k, l)| + |I hl (k, l)| + |I hh (k, l)|, (2.12)k,lwhere I lh (k, l) is the low-high subimage, I hl (k, l) is the high-low subimage, andI hh (k, l) is the high-high subimage of the wavelet transform, respectively, andM × N is the number of pixels in the fire-colored moving region. If the decisionparameter of the fourth step of the algorithm, v 4 , exceeds a threshold, then it islikely that this moving and fire-colored region under investigation is a fire region.

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