Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 17Table 2.1: The mean red, green and blue channel values and variances of tenGaussian distributions modeling flame color in Fig. 2.2 (b) are listed.Distribution Red Green Blue Variance1 121.16 76.87 43.98 101.162 169.08 84.14 35.20 102.773 177.00 104.00 62.00 100.004 230.42 113.78 43.71 107.225 254.47 214.66 83.08 100.116 254.97 159.06 151.00 100.087 254.98 140.98 141.93 100.398 254.99 146.95 102.99 99.579 255.00 174.08 175.01 101.0110 255.00 217.96 176.07 100.78wherec = η(Q n |µ d,n , Σ d,n ) (2.11)A Gaussian mixture model with ten Gaussian distributions is presented inFig. 2.2 (b). In this figure, spheres centered at the mean values of Gaussianshave radii twice the corresponding standard deviations. The mean red, greenand blue values and variances of Gaussian distributions in Fig. 2.2 (b) are listedin Table 2.1.Once flame pixel process is modeled and fixed in the training phase, theRGB color vector of a pixel is checked whether the pixel lies within two standarddeviations of the centers of the Gaussians to determine its nature. In other words,if a given pixel color value is inside one of the spheres shown in Fig. 2.2 (b), thenit is assumed to be a fire colored pixel. We set a binary mask, called FireColored,which returns whether a given pixel is fire colored or not. The intersection of thismask with Blobs formed in the first step is fed into the next step as a new binarymask called Fire.