Browsing by Subject "Flame detection"

Now showing 1 - 10 of 10

Open Access
Compressive sensing based flame detection in infrared videos
(IEEE, 2013) Günay, Osman; Çetin, A. Enis
In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.
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
Diferansiyel PIR algılayıcılarla dalgacık tabanlı alev tespiti
(IEEE, 2012-04) Erden, F.; Töreyin, B. U.; Soyer, E. B.; İnaç, İ.; Günay, O.; Köse, K.; Çetin, A. Enis
Bu makalede, diferansiyel kızılberisi algılayıcı (PIR) kullanılarak geliştirilen bir alev tespit sistemi önerilmektedir. Diferansiyel kızılberisi algılayıcılar, yalnızca görüş alanlarındaki ani sıcaklık değişikliklerine duyarlıdır ve zamanla değişen sinyaller üretir. Algılayıcı sinyaline ait dalgacık dönüşümü, öznitelik çıkarmak için kullanılır ve bu öznitelik vektörü hızlı titreşen kontrolsüz bir ateşin alevi ve bir kişinin yürümesi olaylarıyla eğitilmiş Markov modellerine sokulur. En yüksek olasılıkla sonuçlanan modele karar verilir. Karşılaştırmalı sonuçlar, sistemin geniş odalarda ateş tespiti için kullanılabileceğini düşündürmektedir.
Open Access
Flame detection for video-based early fire warning for the protection of cultural heritage
(2012-10-11) Dimitropoulos, K.; Günay, Osman; Köse, Kıvanç; Erden, Fatih; Chaabene, F.; Tsalakanidou, F.; Grammalidis, N.; Çetin, Enis
Cultural heritage and archaeological sites are exposed to the risk of fire and early warning is the only way to avoid losses and damages. The use of terrestrial systems, typically based on video cameras, is currently the most promising solution for advanced automatic wildfire surveillance and monitoring. Video cameras are sensitive in visible spectra and can be used either for flame or smoke detection. This paper presents and compares three video-based flame detection techniques, which were developed within the FIRESENSE EU research project. © 2012 Springer-Verlag Berlin Heidelberg.
Open Access
Flame detection in video using hidden Markov models
(IEEE, 2005) Töreyin, B. Uğur; Dedeoğlu, Yiğithan; Çetin, A. Enis
This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.
Open Access
Flame detection method in video using covariance descriptors
(IEEE, 2011) Habiboǧlu, Y.H.; Günay, Osman; Çetin, A. Enis
Video fire detection system which uses a spatio-temporal covariance matrix of video data is proposed. This system divides the video into spatio-temporal blocks and computes covariance features extracted from these blocks to detect fire. Feature vectors taking advantage of both the spatial and the temporal characteristics of flame colored regions are classified using an SVM classifier which is trained and tested using video data containing flames and flame colored objects. Experimental results are presented. © 2011 IEEE.
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
Wavelet based flickering flame detector using differential PIR sensors
(Elsevier, 2012-07-06) Erden, F.; Toreyin, B. U.; Soyer, E. B.; Inac, I.; Gunay, O.; Kose, K.; Çetin, A. Enis
A Pyro-electric Infrared (PIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the PIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms.