Browsing by Subject "Background subtraction"
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Item Open Access Background subtraction with a moving camera(IEEE, 2013) Topçu O.; Kalem, Aslıhan; Esen, E.Moving object segmentation with a nonstationary camera is a difficult problem due to the motion of both camera and the object. A moving object segmentation method is proposed in this work to be used in pan-tilt-zoom (PTZ) cameras. The method is based on composing scene mosaic and applying Gaussian mixture background subtraction algorithm after constructing a background model using the mosaic. Background subtraction is performed by mapping the frames captured during camera's course of motion to the background mosaic. The proposed mosaic building method requires less number of picture correspondences when compared to known methods. The success of the proposed segmentation method is demonstrated by the conducted experiments. © 2013 IEEE.Item Open Access Convexity in source separation: Models, geometry, and algorithms(Institute of Electrical and Electronics Engineers Inc., 2014) McCoy, M. B.; Cevher, V.; Dinh, Q. T.; Asaei, A.; Baldassarre, L.Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, blind deconvolution, and even dictionary learning. Despite the recent progress in each of these applications, advances in high-throughput sensor technology place demixing algorithms under pressure to accommodate extremely high-dimensional signals, separate an ever larger number of sources, and cope with more sophisticated signal and mixing models. These difficulties are exacerbated by the need for real-time action in automated decision-making systems. © 1991-2012 IEEE.Item Open Access Real-time wildfire detection using correlation descriptors(IEEE, 2011) Habiboğlu, Y. Hakan; Günay, Osman; Çetin, A. EnisA video based wildfire detection system that based on spatio-temporal correlation descriptors is developed. During the initial stages of wildfires smoke plume becomes visible before the flames. The proposed method uses background subtraction and color thresholds to find the smoke colored slow moving regions in video. These regions are divided into spatio-temporal blocks and correlation features are extracted from the blocks. Property sets that represent both the spatial and the temporal characteristics of smoke regions are used to form correlation descriptors. An SVM classifier is trained and tested with descriptors obtained from video data containing smoke and smoke colored objects. Experimental results are presented. © 2011 EURASIP.Item Open Access Video gözetleme uygulamalarında kamera sabotaj sezimi(IEEE, 2007-06) Aksay, A.; Temizel, A.; Çetin, A. EnisSon yıllarda video gözetim uygulamaları için kullanılan kamera sayılarında kayda değer artış olmuştur. Bu kameraların amaçlandığı gibi çalışıyor olması anlamlı bilgi yakalaması önemlidir. Suç işleyecek kişiler kamera önünü kapatarak, boya püskürterek ya da kamera odağını bozarak görüntülerinin ve eylemlerinin kaydedilmesini engellemek yoluna başvurmaktadır. Kameraları gözleyen birisinin olmaması ya da dikkatinin dağınık olması sabotajın fark edilememesine neden olur ve sistem normal olarak çalışıyor ve kaydediyor olsa bile kayıtların kullanılamaz olmasına yolaçar. Bu bildiride, dalgacık alanında arkaplan çıkarımı yöntemi kullanılarak kamerada görüş azalması ve kamera önünün kapatılması durumlarının gerçek zamanlı kestirimi önerilmektedir. Ayrıca, sistemin gerçek hayat koşullarında daha güvenilir çalışması için bazı yöntemler de önerilmiştir.