Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 45Similar to the discussions in Chapter 2, flame flicker process in IR video ischaracterized by a random Markov model. As in the previous chapter, threestateMarkov models are trained off-line for both flame and non-flame pixels torepresent the temporal behavior (cf. Fig. 4.3). These models are trained using afeature signal which is defined as follows: Let I k (n) be the intensity value of thek − th pixel at frame n. The wavelet coefficients of I k are obtained by the samestructure shown in Fig. 4.2, but filtering is implemented temporally.Figure 3.7: Three-state Markov models for a) flame and b) non-flame movingpixels.The states of HMMs are defined as follows: at time n, if |w(n)| < T 1 , thestate is in S1; if T 1 < |w(n)| < T 2 , the state is S2; else if |w(n)| > T 2 , the stateS3 is attained. For the pixels of regular hot objects like walking people, engineof a moving car, etc., no rapid changes take place in the pixel values. When thewavelet coefficients fluctuate between values above the higher threshold T 2 andbelow the lower threshold T 1 in a frequent manner this indicates the existence offlames in the viewing range of the IR camera.The transition probabilities between states for a pixel are estimated during apre-determined period of time around flame boundaries. During the recognitionphase, the HMM based analysis is carried out in pixels near the contour boundariesof bright moving regions whose ρ values exceed ρ T . The state sequence of