Fire Detection Algorithms Using Multimodal ... - Bilkent University

Chapter 1IntroductionDynamic textures are common in many image sequences of natural scenes. Examplesof dynamic textures in video include fire, smoke, clouds, volatile organiccompound (VOC) plumes in infra-red (IR) videos, trees in the wind, sea andocean waves, as well as traffic scenes, motion of crowds, all of which exhibit somesort of spatio-temporal stationarity. They are also named as temporal or 3-D texturesin the literature. Researchers extensively studied 2-D textures and relatedproblems in the fields of image processing and computer vision [32], [30]. Onthe other hand, there is comparably less research conducted on dynamic texturedetection in video [18], [63], [12].There are several approaches in the computer vision literature aiming atrecognition and synthesis of dynamic textures in video independent of theirtypes [71], [15], [16], [50], [29], [51], [33], [35], [89], [48], [49], [57], [79], [55],[96], [62], [75], [28], [68], [90]. Some of these approaches model the dynamictextures as linear dynamical systems [71], [15], [16], [50], some others use spatiotemporalauto-regressive models [48], [79]. Other researchers in the field analyzeand model the optical flow vectors for the recognition of generic dynamic texturesin video [29], [89]. In this dissertation, we do not attempt to characterizeall dynamic textures but we present smoke and fire detection methods by takingadvantage of specific properties of smoke and fire.1