Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 1. INTRODUCTION 3No fire detection experiments were carried out using other sensing modalitiessuch as ultra-violet (UV) sensors, near infrared (NIR) or middle wave-lengthinfrared (MWIR) cameras, in this study. As a matter of fact, MWIR camerasare even more expensive than LWIR cameras. Therefore, deploying MWIR camerasfor fire monitoring turns out to be an unfeasible option for most practicalapplications.There are built-in microphones in most of the off-the-shelf surveillance cameras.Audio data captured from these microphones can be also analyzed alongwith the video data in fire monitoring applications. One can develop fire detectionmethods exploiting data coming from several sensing modalities similar tomethods described in [31], [85], [88], [24].1.1 Contribution of this ThesisThe major contributions of this thesis can be divided into two main categories.1.1.1 Markov Models Using Wavelet Domain Features forShort Range Flame and Smoke DetectionA common feature of all the algorithms developed in this thesis is the use ofwavelets and Markov models. In the proposed approach wavelets or sub-bandanalysis are used in dynamic texture modeling. This leads to computationallyefficient algorithms for texture feature analysis, because computing wavelet coefficientsis an Order-(N) type operation. In addition, we do not try to determineedges or corners in a given scene. We simply monitor the decay or increase inwavelet coefficients’ sub-band energies both temporally and spatially.Another important feature of the proposed smoke and fire detection methods isthe use of Markov models to characterize temporal motion in the scene. Turbulentfire behavior is a random phenomenon which can be conveniently modeled in a