Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 6. WILDFIRE DETECTION 90The confidence value of this sub-algorithm is defined according to the angleand magnitudes of average color vectors, c⃗I,S (n) and c B,S ⃗ (n). The decision functionD 4 (x, n) corresponding to this sub-algorithm for a pixel in the n − th imageand background frames is given by:D 4 (x, n) ={ 4|θ(x)|π− 1, if | c⃗I,S (n)| < | c B,S ⃗ (n)|−1, if | c⃗I,S (n)| > | c B,S ⃗ (n)|(6.8)where θ(x) is the angle between the two color vectors. When the angle betweenthe two color vectors are close to each other, the function D 4 (x, n) is close to−1 which corresponds to shadow regions. Similar decision functions for shadowdetection can be defined according to other color spaces including the Y UV space.There are other shadow detection algorithms in the literature [66]. However,we selected the algorithm described in this section, because of its low computationalcomplexity. Our aim is to realize a wildfire detection system working inreal-time.The threshold values in all of the decision functions described in this sectionare chosen in such a way that they produce positive values for all of the wild firevideo recordings that we have. Still, one can define other sets of decision functionswith different threshold values representing various threat/security levels. In thestandard monitoring mode without any fires in the viewing range of the cameras,the security level may be kept as ‘low’. Once a fire is detected, the systemcan automatically switch to security level ‘high’ and increase the probability ofdetecting possible fires that may follow the initial one, by lowering the thresholdsin the decision functions.Decision results of four sub-algorithms, D 1 , D 2 , D 3 and D 4 are linearly combinedto reach a final decision on a given pixel whether it is a pixel of a smokeregion or not. Equal weights could be assigned to each sub-algorithm, however,this would yield a non-adaptive algorithm without any learning capability. Onthe other hand, wildfire detection is actually a dynamic process. There maybe wild variations between forestal areas and substantial temporal changes mayoccur within the same forestal region. An adaptive combination of different subalgorithmswould be more appropriate for robust wildfire detection. In the next