Browsing by Author "Töreyin, B. U."

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Open Access
Adaptive decision fusion based cooperative spectrum sensing for cognitive radio systems
(IEEE, 2011) Töreyin, B. U.; Yarkan, S.; Qaraqe, K. A.; Çetin, A. Enis
In this paper, an online Adaptive Decision Fusion (ADF) framework is proposed for the central spectrum awareness engine of a spectrum sensor network in Cognitive Radio (CR) systems. Online learning approaches are powerful tools for problems where drifts in concepts take place. Cooperative spectrum sensing in cognitive radio networks is such a problem where channel characteristics and utilization patterns change frequently. The importance of this problem stems from the requirement that secondary users must adjust their frequency utilization strategies in such a way that the communication performance of the primary users would not be degraded by any means. In the proposed framework, sensing values from several sensor nodes are fused together by weighted linear combination at the central spectrum awareness engine. The weights are updated on-line according to an active fusion method based on performing orthogonal projections onto convex sets describing power reading values from each sensor. The proposed adaptive fusion strategy for cooperative spectrum sensing can operate independent from the channel type between the primary user and secondary users. Results of simulations and experiments for the proposed method conducted in laboratory are also presented. © 2011 IEEE.
Open Access
Computer vision based method for real-time fire and flame detection
(Elsevier BV, 2006-01-01) Töreyin, B. U.; Dedeoǧlu, Y.; Güdükbay, Uğur; Çetin, A. Enis
This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues. © 2005 Elsevier B.V. All rights reserved.
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
Early wildfire smoke detection based on motion-based geometric image transformation and deep convolutional generative adversarial networks
(Institute of Electrical and Electronics Engineers Inc., 2019) Aslan, Süleyman; Güdükbay, Uğur; Töreyin, B. U.; Çetin, A. Enis
Early detection of wildfire smoke in real-time is essentially important in forest surveillance and monitoring systems. We propose a vision-based method to detect smoke using Deep Convolutional Generative Adversarial Neural Networks (DC-GANs). Many existing supervised learning approaches using convolutional neural networks require substantial amount of labeled data. In order to have a robust representation of sequences with and without smoke, we propose a two-stage training of a DCGAN. Our training framework includes, the regular training of a DCGAN with real images and noise vectors, and training the discriminator separately using the smoke images without the generator. Before training the networks, the temporal evolution of smoke is also integrated with a motion-based transformation of images as a pre-processing step. Experimental results show that the proposed method effectively detects the smoke images with negligible false positive rates in real-time.
Open Access
Entropi fonsiyonuna dayalı uyarlanır karar tümleştirme yapısı
(2012-04) Günay, Osman; Töreyin, B. U.; Köse, Kıvanç; Çetin, A. Enis
Bu bildiride, resim analizi ve bilgisayarla görü uygulamalarında kullanılmak üzere entropi fonksiyonuna dayanan uyarlanır karar tümleştirme yapısı geliştirilmiştir. Bu yapıda bileşik algoritma, herbiri güven derecesini temsil eden sıfır merkezli bir gerçek sayı olarak kendi kararını oluşturan birçok alt algoritmadan meydana gelir. Karar değerleri, çevrimiçi olarak alt algoritmaları tanımlayan dışbukey kümelerin üzerine entropik izdüşümler yapmaya dayalı bir aktif tümleştirme yöntemi ile güncellenen ağırlıklar kullanılarak doğrusal olarak birleştirilir. Bu yapıda genelde bir insan olan bir uzman da bulunur ve karar tümleştirme algoritmasına geribesleme sağlar. Önerilen karar tümleştirme algoritmasının performansı geliştirdigimiz video tabanlı bir orman yangını bulma sistemi kullanılarak test edilmiştir.
Open Access
An experimental validation of an online adaptive cooperation scheme for spectrum sensing
(IEEE, 2011-05) Yarkan, S.; Töreyin, B. U.; Qaraqe, K. A.; Çetin, A. Enis
Cooperative spectrum sensing methods in the literature assume a static communication scenario with fixed channel and propagation environment characteristics. In order to maintain the level of sensing reliability and performance under changing channel and environment conditions, in this study, an online adaptive cooperation scheme is proposed. Energy detection data from each cooperating sensor are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projections onto convex sets (POCS). Also, in this paper, an end-to-end methodology for a flexible experimental setup is proposed. This setup is specifically deployed to emulate the proposed adaptive cooperation scheme for spectrum sensing and validate its practical use in cognitive radio systems. © 2011 IEEE.
Open Access
Fall detection using single-tree complex wavelet transform
(Elsevier, 2013) Yazar, A.; Keskin, F.; Töreyin, B. U.; Çetin, A. Enis
The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.
Open Access
Fire detection in infrared video using wavelet analysis
(SPIE - International Society for Optical Engineering, 2007) Töreyin, B. U.; Cinbiş, R. G.; Dedeoğlu, Y.; Çetin, A. Enis
A novel method to detect flames in infrared (IR) video is proposed. Image regions containing flames appear as bright regions in IR video. In addition to ordinary motion and brightness clues, the flame flicker process is also detected by using a hidden Markov model (HMM) describing the temporal behavior. IR image frames are also analyzed spatially. Boundaries of flames are represented in wavelet domain and the high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker. All of the temporal and spatial clues extracted from the IR video are combined to reach a final decision. False alarms due to ordinary bright moving objects are greatly reduced because of the HMM-based flicker modeling and wavelet domain boundary modeling.
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
İnsan hareketlerinin vibrasyon ve PIR algılayıcıları kullanılarak sınıflandırılması
(IEEE, 2012-04) Yazar, Ahmet; Çetin, A. Enis; Töreyin, B. U.
Yalnız yaşayan yaşlı ve bakıma muhtaç kişilerin yere düşmelerinin kısa sürede tespit edilmesi önemli bir sorundur. Bu bildiride, sismik ve pasif kızılberisi algılayıcılar kullanarak, belirli bir alanda yere düşme olayının tespit edilebilmesi için bir yöntem önerilmiştir. Sınıflandırma amacıyla, hızlı Fourier dönüşümü, mel-frekansı kepstral katsayıları ve ayrık dalgacık dönüşümü teknikleri kullanılarak farklı öznitelik vektörleri çıkarılmıştır. Sismik işaretler, destek vektör makineleri ile ‘düşme’ ve ‘düşme değil’ olarak iki farklı sınıfa ayrılmıştır. Pasif kızılberisi algılayıcının, belirli bir alanda hareket eden bir kişiyi tespit etmesinden sonra, sismik algılayıcının ve pasif kızılberisi algılayıcının kararları tümleştirilerek kişinin düşüp düşmediği tespit edilmektedir. Önerdiğimiz sistem gerçek zamanlı olarak standart bir bilgisayarda çalışabilmektedir.
Open Access
Moving object detection in wavelet compressed video
(Elsevier, 2005) Töreyin, B. U.; Çetin, A. Enis; Aksay, A.; Akhan, M. B.
In many surveillance systems the video is stored in wavelet compressed form. In this paper, an algorithm for moving object and region detection in video which is compressed using a wavelet transform (WT) is developed. The algorithm estimates the WT of the background scene from the WTs of the past image frames of the video. The WT of the current image is compared with the WT of the background and the moving objects are determined from the difference. The algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background. This leads to a computationally efficient method and a system compared to the existing motion estimation methods. © 2005 Published by Elsevier B.V.
Open Access
Moving region detection in compressed video
(Springer, 2004) Töreyin, B. U.; Çetin, A. Enis; Aksay, A.; Akhan, M. B.
In this paper, an algorithm for moving region detection in compressed video is developed. It is assumed that the video can be compressed either using the Discrete Cosine Transform (DOT) or the Wavelet Transform (WT). The method estimates the WT of the background scene from the WTs of the past image frames of the video. The WT of the current image is compared with the WT of the background and the moving objects are determined from the difference. The algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background. In the case of DOT compressed video, the DC values of 8 by 8 image blocks of Y, U and V channels are used for estimating the background scene. This leads to a computationally efficient method and a system compared to the existing motion detection methods. © Springer-Verlag 2004.
Open Access
An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method
(Institute of Electrical and Electronics Engineers, 2012) Yarkan S.; Töreyin, B. U.; Qaraqe, K. A.; Çetin, A. Enis
Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors.
Open Access
Silhouette-based method for object classification and human action recognition in video
(Springer, 2006) Dedeoǧlu, Y.; Töreyin, B. U.; Güdükbay, Uğur; Çetin, A. Enis
In this paper we present an instance based machine learning algorithm and system for real-time object classification and human action recognition which can help to build intelligent surveillance systems. The proposed method makes use of object silhouettes to classify objects and actions of humans present in a scene monitored by a stationary camera. An adaptive background subtracttion model is used for object segmentation. Template matching based supervised learning method is adopted to classify objects into classes like human, human group and vehicle; and human actions into predefined classes like walking, boxing and kicking by making use of object silhouettes.
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.