Çokkipli işaret ve imge çözümleme tabanlı yangın tespit algoritmaları

Dynamic textures are common in natural scenes. Examples of dynamic tex-tures in video include fire, smoke, clouds, volatile organic compound (VOC)plumes in infra-red (IR) videos, trees in the wind, sea and ocean waves, etc.Researchers extensively studied 2-D textures and related problems in the fieldsof image processing and computer vision. On the other hand, there is very littleresearch on dynamic texture detection in video. In this dissertation, signal andimage processing methods developed for detection of a specific set of dynamictextures are presented.Signal and image processing methods are developed for the detection of flamesand smoke in open and large spaces with a range of up to 30m to the camera invisible-range (IR) video. Smoke is semi-transparent at the early stages of fire.Edges present in image frames with smoke start loosing their sharpness and thisleads to an energy decrease in the high-band frequency content of the image.Local extrema in the wavelet domain correspond to the edges in an image.The decrease in the energy content of these edges is an important indicatorof smoke in the viewing range of the camera. Image regions containing flames appear asfire-colored (bright) moving regions in (IR) video. In addition to motion andcolor (brightness) clues, the flame flicker process is also detected by using a Hid-den Markov Model (HMM) describing the temporal behavior. Image frames arealso analyzed spatially. Boundaries of flames are represented in wavelet domain.High frequency nature of the boundaries of fire regions is also used as a clue tomodel the flame flicker. Temporal and spatial clues extracted from the video arecombined to reach a final decision.Signal processing techniques for the detection of flames with pyroelectric (pas-sive) infrared (PIR) sensors are also developed. The flame flicker process of anuncontrolled fire and ordinary activity of human beings and other objects aremodeled using a set of Markov models, which are trained using the wavelet trans-form of the PIR sensor signal. Whenever there is an activity within the viewingrange of the PIR sensor, the sensor signal is analyzed in the wavelet domain andthe wavelet signals are fed to a set of Markov models. A fire or no fire decision ismade according to the Markov model producing the highest probability.Smoke at far distances (> 100m to the camera) exhibits different temporal andspatial characteristics than nearby smoke and fire. This demands specific methodsexplicitly developed for smoke detection at far distances rather than using nearbysmoke detection methods. An algorithm for vision-based detection of smoke dueto wild fires is developed. The main detection algorithm is composed of foursub-algorithms detecting (i) slow moving objects, (ii) smoke-colored regions, (iii)rising regions, and (iv) shadows. Each sub-algorithm yields its own decision as azero-mean real number, representing the confidence level of that particular sub-algorithm. Confidence values are linearly combined for the final decision.Another contribution of this thesis is the proposal of a framework for activefusion of sub-algorithm decisions. Most computer vision based detection algo-rithms consist of several sub-algorithms whose individual decisions are integratedto reach a final decision. The proposed adaptive fusion method is based on theleast-mean-square (LMS) algorithm. The weights corresponding to individualsub-algorithms are updated on-line using the adaptive method in the training(learning) stage. The error function of the adaptive training process is definedas the difference between the weighted sum of decision values and the decisionof an oracle who may be the user of the detector. The proposed decision fusionmethod is used in wildfire detection.