Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 4. SHORT RANGE SMOKE DETECTION IN VIDEO 59Figure 4.2: Single-stage wavelet filter bank.values in the background image.The turbulent characteristic of smoke is also used as an additional information.The candidate regions are checked whether they continuously appear anddisappear over time. In general, a pixel especially at the edge of a smoke becomespart of smoke and disappears in the background several times in one second of avideo at random. This characteristic behavior is very well suited to be modeledas a random Markov model.Similar to flame detection methods presented in the previous chapters, threestateMarkov models are temporally trained for both smoke and non-smoke pixels(cf.Fig.4.3) using a wavelet based feature signal. These models are trained usinga feature signal which is defined as follows: Let I(x, n) be the intensity value ofa pixel at location x in the image frame at time step n. The wavelet coefficientsof I are obtained by the filter bank structure shown in Fig.4.2. Non-negativethresholds T 1 < T 2 introduced in wavelet domain, define the three states of thehidden Markov models for smoke and non-smoke moving objects. At time n,if |w(n)| < T 1, the state is in F 1; if T 1 < |w(n)| < T 2, the state is F 2; elseif |w(n)| > T 2, the state Out is attained. The transition probabilities betweenstates for a pixel are estimated during a pre-determined period of time aroundsmoke boundaries. In this way, the model not only learns the turbulent behaviorof smoke boundaries during a period of time, but also it tailors its parameters tomimic the spatial characteristics of smoke regions.