Browsing by Subject "Fire detectors."
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Item Open Access CUDA based implementation of flame detection algorithms in day and infrared camera videos(2011) Hamzaçebi, HasanAutomatic fire detection in videos is an important task but it is a challenging problem. Video based high performance fire detection algorithms are important for the detection of forest fires. The usage area of fire detection algorithms can further be extended to the places like state and heritage buildings, in which surveillance cameras are installed. In uncontrolled fires, early detection is crucial to extinguish the fire immediately. However, most of the current fire detection algorithms either suffer from high false alarm rates or low detection rates due to the optimization constraints for real-time performance. This problem is also aggravated by the high computational complexity in large areas, where multicamera surveillance is required. In this study, our aim is to speed up the existing color video fire detection algorithms by implementing in CUDA, which uses the parallel computational power of Graphics Processing Units (GPU). Our method does not only speed up the existing algorithms but it can also reduce the optimization constraints for real-time performance to increase detection probability without affecting false alarm rates. In addition, we have studied several methods that detect flames in infrared video and proposed an improvement for the algorithm to decrease the false alarm rate and increase the detection rate of the fire.Item Open Access Fire and flame detection methods in images and videos(2010) Habiboğlu, Yusuf HakanIn this thesis, automatic fire detection methods are studied in color domain, spatial domain and temporal domain. We first investigated fire and flame colors of pixels. Chromatic Model, Fisher’s linear discriminant, Gaussian mixture color model and artificial neural networks are implemented and tested for flame color modeling. For images a system that extracts patches and classifies them using textural features is proposed. Performance of this system is given according to different thresholds and different features. A real-time detection system that uses information in color, spatial and temporal domains is proposed for videos. This system, which is develop by modifying previously implemented systems, divides video into spatiotemporal blocks and uses features extracted from these blocks to detect fire.Item Open Access Fire detection algorithms using multimodal signal and image analysis(2009) Töreyin, Behçet UğurDynamic 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.