Browsing by Subject "dynamic textures"

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Open Access
Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection
(2009) Günay, Osman
Dynamic textures are moving image sequences that exhibit stationary characteristics in time such as fire, smoke, volatile organic compound (VOC) plumes, waves, etc. Most surveillance applications already have motion detection and recognition capability, but dynamic texture detection algorithms are not integral part of these applications. In this thesis, image processing based algorithms for detection of specific dynamic textures are developed. Our methods can be developed in practical surveillance applications to detect VOC leaks, fire and smoke. The method developed for VOC emission detection in infrared videos uses a change detection algorithm to find the rising VOC plume. The rising characteristic of the plume is detected using a hidden Markov model (HMM). The dark regions that are formed on the leaking equipment are found using a background subtraction algorithm. Another method is developed based on an active learning algorithm that is used to detect wild fires at night and close range flames. The active learning algorithm is based on the Least-Mean-Square (LMS) method. Decisions from the sub-algorithms, each of which characterize a certain property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect fire and smoke from moving camera video sequences. The global motion of the camera is compensated by finding an affine transformation between the frames using optical flow and RANSAC. Three frame change detection methods with motion compensation are used for fire detection with a moving camera. A background subtraction algorithm with global motion estimation is developed for smoke detection.
Open Access
Fire detection algorithms using multimodal signal and image analysis
(2009) Töreyin, Behçet Uğur
Dynamic textures are common in natural scenes. Examples of dynamic textures 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 fields of image processing and computer vision. On the other hand, there is very little research on dynamic texture detection in video. In this dissertation, signal and image processing methods developed for detection of a specific set of dynamic textures are presented. Signal and image processing methods are developed for the detection of flames and smoke in open and large spaces with a range of up to 30m to the camera in visible-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 this leads 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 indicator of smoke in the viewing range of the camera. Image regions containing flames appear as fire-colored (bright) moving regions in (IR) video. In addition to motion and color (brightness) clues, the flame flicker process is also detected by using a Hidden Markov Model (HMM) describing the temporal behavior. Image frames are also 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 to model the flame flicker. Temporal and spatial clues extracted from the video are combined to reach a final decision.Signal processing techniques for the detection of flames with pyroelectric (passive) infrared (PIR) sensors are also developed. The flame flicker process of an uncontrolled fire and ordinary activity of human beings and other objects are modeled using a set of Markov models, which are trained using the wavelet transform of the PIR sensor signal. Whenever there is an activity within the viewing range of the PIR sensor, the sensor signal is analyzed in the wavelet domain and the wavelet signals are fed to a set of Markov models. A fire or no fire decision is made according to the Markov model producing the highest probability. Smoke at far distances (> 100m to the camera) exhibits different temporal and spatial characteristics than nearby smoke and fire. This demands specific methods explicitly developed for smoke detection at far distances rather than using nearby smoke detection methods. An algorithm for vision-based detection of smoke due to wild fires is developed. The main detection algorithm is composed of four sub-algorithms detecting (i) slow moving objects, (ii) smoke-colored regions, (iii) rising regions, and (iv) shadows. Each sub-algorithm yields its own decision as a zero-mean real number, representing the confidence level of that particular subalgorithm. Confidence values are linearly combined for the final decision. Another contribution of this thesis is the proposal of a framework for active fusion of sub-algorithm decisions. Most computer vision based detection algorithms consist of several sub-algorithms whose individual decisions are integrated to reach a final decision. The proposed adaptive fusion method is based on the least-mean-square (LMS) algorithm. The weights corresponding to individual sub-algorithms are updated on-line using the adaptive method in the training (learning) stage. The error function of the adaptive training process is defined as the difference between the weighted sum of decision values and the decision of an oracle who may be the user of the detector. The proposed decision fusion method is used in wildfire detection.