Fire Detection Algorithms Using Multimodal ... - Bilkent University
Fire Detection Algorithms Using Multimodal ... - Bilkent University Fire Detection Algorithms Using Multimodal ... - Bilkent University
CHAPTER 1. INTRODUCTION 7security guard of the forest look-out tower.The contribution of this work is twofold; a novel video based wildfire detectionmethod and a novel active learning framework based on the LMS algorithm. Theproposed adaptive fusion strategy can be used in many supervised learning basedcomputer vision applications comprising of several sub-algorithms.1.2 Thesis OutlineThe outline of the thesis is as follows. In Chapters 2 and 3, wavelet and HMMbased methods for flame detection in visible and IR range video are presented,respectively. The short-range smoke detection algorithm is presented in Chapter4. Detection of flames using PIR sensors is discussed in Chapter 5. In Chapter6, wildfire (long-range smoke) detection with active learning based on the LMSalgorithm is described. Finally Chapter 7 concludes this thesis by providing anoverall summary of the results. Possible research areas in the future are provided,as well.The organization of this thesis is presented in Table 1.1. Note that, smokedetection methods could only be developed for visible range cameras due to thefact that smoke cannot be visualized with PIR sensors and LWIR cameras.Table 1.1: Organization of this thesis.Sensor type Flame Short-range (< 30m) Long distance (> 100m)SmokeSmokeVisible Range Camera Chapter 2 Chapter 4 Chapter 6LWIR Camera Chapter 3 N/A N/APIR Sensor Chapter 5 N/A N/A
Chapter 2Flame Detection in VisibleRange VideoIn this chapter, the previous work in the literature on video based fire detection issummarized, first. Then, the proposed wavelet analysis and Markov model baseddetection method characterizing the flame flicker process is described. Markovmodel based approach reduces the number of false alarms issued to ordinaryfire-colored moving objects as compared to the methods using only motion andcolor clues. Experimental results show that the proposed method is successful indetecting flames.2.1 Related WorkVideo based fire detection systems can be useful for detecting fire in covered areasincluding auditoriums, tunnels, atriums, etc., in which conventional chemicalfire sensors cannot provide quick responses to fire. Furthermore, closed circuittelevision (CCTV) surveillance systems are currently installed in various publicplaces monitoring indoors and outdoors. Such systems may gain an early fire detectioncapability with the use of a fire detection software processing the outputsof CCTV cameras in real time.8
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Chapter 2Flame <strong>Detection</strong> in VisibleRange VideoIn this chapter, the previous work in the literature on video based fire detection issummarized, first. Then, the proposed wavelet analysis and Markov model baseddetection method characterizing the flame flicker process is described. Markovmodel based approach reduces the number of false alarms issued to ordinaryfire-colored moving objects as compared to the methods using only motion andcolor clues. Experimental results show that the proposed method is successful indetecting flames.2.1 Related WorkVideo based fire detection systems can be useful for detecting fire in covered areasincluding auditoriums, tunnels, atriums, etc., in which conventional chemicalfire sensors cannot provide quick responses to fire. Furthermore, closed circuittelevision (CCTV) surveillance systems are currently installed in various publicplaces monitoring indoors and outdoors. Such systems may gain an early fire detectioncapability with the use of a fire detection software processing the outputsof CCTV cameras in real time.8
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