Browsing by Subject "Fire detection"

Now showing 1 - 9 of 9

Open Access
Covariance matrix-based fire and flame detection method in video
(Springer, 2011-09-17) Habiboğlu, Y. H.; Günay, O.; Çetin, A. Enis
This paper proposes a video-based fire detection system which uses color, spatial and temporal information. The system divides the video into spatio-temporal blocks and uses covariance-based features extracted from these blocks to detect fire. Feature vectors take advantage of both the spatial and the temporal characteristics of flame-colored regions. The extracted features are trained and tested using a support vector machine (SVM) classifier. The system does not use a background subtraction method to segment moving regions and can be used, to some extent, with non-stationary cameras. The computationally efficient method can process 320×240 video frames at around 20 frames per second in an ordinary PC with a dual core 2.2 GHz processor. In addition, it is shown to outperform a previous method in terms of detection performance.
Open Access
Deep convolutional generative adversarial networks for flame detection in video
(Springer, Cham, 2020) Aslan, Süleyman; Güdükbay, Uğur; Töreyin, B. U.; Çetin, A. Enis
Real-time flame detection is crucial in video-based surveillance systems. We propose a vision-based method to detect flames using Deep Convolutional Generative Adversarial Neural Networks (DCGANs). Many existing supervised learning approaches using convolutional neural networks do not take temporal information into account and require a substantial amount of labeled data. To have a robust representation of sequences with and without flame, we propose a two-stage training of a DCGAN exploiting spatio-temporal flame evolution. Our training framework includes the regular training of a DCGAN with real spatio-temporal images, namely, temporal slice images, and noise vectors, and training the discriminator separately using the temporal flame images without the generator. Experimental results show that the proposed method effectively detects flame in video with negligible false-positive rates in real-time.
Open Access
Dynamic texture detection, segmentation and analysis
(ACM, 2007-07) Töreyin, Behçet Uğur; Dedeoğlu, Yiğithan; Çetin, A. Enis; Fazekas, S.; Chetverikov, D.; Amiaz, T.; Kiryati, N.
Dynamic textures are common in natural scenes. Examples of dynamic textures in video include fire, smoke, clouds, trees in the wind, sky, sea and ocean waves etc. In this showcase, (i) we develop real-time dynamic texture detection methods in video and (ii) present solutions to video object classification based on motion information. Copyright 2007 ACM.
Open Access
Fire detection in video using LMS based active learning
(Springer, 2009) Günay, O.; Taşdemir K.; Töreyin, B. U.; Çetin, A. Enis
In this paper, a video based algorithm for fire and flame detection is developed. In addition to ordinary motion and color clues, flame flicker is distinguished from motion of flame colored moving objects using Markov models. Irregular nature of flame boundaries is detected by performing temporal wavelet analysis using Hidden Markov Models as well. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Boundary of flames are represented in wavelet domain and irregular nature of the boundaries of fire regions is also used as an indication of the flame flicker. Decisions from sub-algorithms are linearly combined using an adaptive active fusion method. The main detection algorithm is composed of four sub-algorithms (i) detection of fire colored moving objects, (ii) temporal, and (iii) spatial wavelet analysis for flicker detection and (iv) contour analysis of fire colored region boundaries. Each algorithm yields a continuous decision value as a real number in the range [-1, 1] at every image frame of a video sequence. Decision values from sub-algorithms are fused using an adaptive algorithm in which weights are updated using the least mean square (LMS) method in the training (learning) stage.
Open Access
Flame detection system based on wavelet analysis of PIR sensor signals with an HMM decision mechanism
(IEEE, 2008-08) Ug̃ur Töreyin, B.; Soyer, E. Birey; Urfaliog̃lu, Onay; Çetin, A. Enis
In this paper, a flame detection system based on a pyroelectric (or passive) infrared (PIR) sensor is described. The flame detection system can be used for fire detection in large rooms. The flame flicker process of an uncontrolled fire and ordinary activity of human beings and other objects are modeled using a set of Hidden Markov Models (HMM), 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 system, the sensor signal is analyzed in the wavelet domain and the wavelet signals are fed to a set of HMMs. A fire or no fire decision is made according to the HMM producing the highest probability. copyright by EURASIP.
Open Access
A multi-modal video analysis approach for car park fire detection
(Elsevier, 2013) Verstockt, S.; Hoecke, S. V.; Beji, T.; Merci, B.; Gouverneur, B.; Çetin, A. Enis; Potter, P. D.; Walle, R. V. D.
In this paper a novel multi-modal flame and smoke detector is proposed for the detection of fire in large open spaces such as car parks. The flame detector is based on the visual and amplitude image of a time-of-flight camera. Using this multi-modal information, flames can be detected very accurately by visual flame feature analysis and amplitude disorder detection. In order to detect the low-cost flame related features, moving objects in visual images are analyzed over time. If an object possesses high probability for each of the flame characteristics, it is labeled as candidate flame region. Simultaneously, the amplitude disorder is also investigated. Also labeled as candidate flame regions are regions with high accumulative amplitude differences and high values in all detail images of the amplitude image's discrete wavelet transform. Finally, when there is overlap of at least one of the visual and amplitude candidate flame regions, fire alarm is raised. The smoke detector, on the other hand, focuses on global changes in the depth images of the time-of-flight camera, which do not have significant impact on the amplitude images. It was found that this behavior is unique for smoke. Experiments show that the proposed detectors improve the accuracy of fire detection in car parks. The flame detector has an average flame detection rate of 93%, with hardly any false positive detection, and the smoke detection rate of the TOF based smoke detector is 88%.
Open Access
Video based fire detection at night
(IEEE, 2009) Taşdemir, Kasım; Günay, Osman; Töreyin, Behçet Uğur; Çetin, A. Enis
There has been increasing interest in the study of video based fire detection as video based surveillance systems become widely available for indoor and outdoor monitoring applications. Video based fire detection methods in computer vision literature do not take into account whether the fire takes place in the day time or at night. A novel method explicitly developed for video based detection of fire at night (in the dark) is presented in this paper. The method comprises three sub-algorithms each of which characterizes certain part of fire at night. Individual decisions of the sub-algorithms are combined together using a least-mean-square based decision fusion approach.
Open Access
Video based wildfire detection at night
(ELSEVIER, 2009-05-06) Günay, O.; Taşdemir K.; Töreyin, B. U.; Çetin, A. Enis
There has been an increasing interest in the study of video based fire detection algorithms as video based surveillance systems become widely available for indoor and outdoor monitoring applications. A novel method explicitly developed for video based detection of wildfires at night (in the dark) is presented in this paper. The method comprises four sub-algorithms: (i) slow moving video object detection, (ii) bright region detection, (iii) detection of objects exhibiting periodic motion, and (iv) a sub-algorithm interpreting the motion of moving regions in video. Each of these sub-algorithms characterizes an aspect of fire captured at night by a visible range PTZ camera. Individual decisions of the sub-algorithms are combined together using a least-mean-square (LMS) based decision fusion approach, and fire/nofire decision is reached by an active learning method.
Open Access
Video fire detection-Review
(Elsevier, 2013) Çetin, A. Enis; Dimitropoulos, K.; Gouverneur, B.; Grammalidis, N.; Günay, O.; Habiboğlu, Y. H.; Töreyin, B. U.; Verstockt, S.
This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor "volumes" and do not have transport delay that the traditional "point" sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation.