Browsing by Subject "Video data"
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Item Open Access Flame detection method in video using covariance descriptors(IEEE, 2011) Habiboǧlu, Y.H.; Günay, Osman; Çetin, A. EnisVideo 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.Item Open Access Real-time fire and flame detection in video(IEEE, 2005) Dedeoğlu, Yigithan; Töreyin, B. Ugur; Güdükbay, Uğur; Çetin, A. EnisThis paper proposes a novel method to detect fire and/or flame 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 wavelet domain. Periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.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.