Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 6. WILDFIRE DETECTION 86D 1 (x, n) is defined as:⎧⎪⎨−1if |B fast (x, n)−B slow (x, n)|≤T lowD 1 (x, n) = 2 |Bfast (x,n)−B slow (x,n)|−T lowT high −T low−1 if T low ≤|B fast (x, n)−B slow (x, n)|≤T high⎪⎩1 if T high ≤|B fast (x, n)−B slow (x, n)|(6.2)where 0 < T low < T high are experimentally determined threshold values. In ourimplementation, T low (T high ) is taken as 10 (30) on the luminance (Y) componentof video.Confidence value is 1 (−1), if the difference |B fast (x, n)−B slow (x, n)| is higher(lower) than threshold T high (T low ).The decision function D 1 (x, n) takes realvalues in the range [-1,1] if the difference is in between the two threshold values.Smoke due to forest fires at further distances (> 5km) to the camera seemto move even slower. Therefore, smoke regions at these distances appear neitherin B fast nor B slow images. This results in lower difference values between backgroundimages B slow and B fast . In order to have substantial difference values anddetect smoke at distances further than 5km to the camera, B fast terms in Eq. 6.2are replaced by the current image I.6.2.2 Detection of Smoke-Colored RegionsWhenever a slow moving region is detected, its color content is analyzed. Smokedue to forest fires is mainly composed of carbon dioxide, water vapor, carbonmonoxide, particulate matter, hydrocarbons and other organic chemicals, nitrogenoxides, trace minerals and some other compounds [2]. The grayish color ofthe rising plume is primarily due to water vapor and carbon particles in the outputfire composition. Such regions can be identified by setting thresholds in theYUV color space. Also, luminance value of smoke regions should be high especiallyat the initial phases of a wildfire, as shown in Fig. 6.1. On the other hand,the chrominance values should be very low in a smoke region. Confidence valuecorresponding to this sub-algorithm should account for these characteristics.