Video surveillance systems are widely applied in a variety of fields. Hence, video-based smoke detection is regarded as an effective and inexpensive way for fire detection in an open or large spaces. In order to improve the efficiency of the video-based smoke detection, a novel video-based smoke detection method is proposed by using a histogram sequence of pyramids. The method involves four steps. Firstly, through multi-scale analysis, a 3-level image pyramid is constructed. Secondly, local binary patterns (LBP), which are insensitive to image rotation and illumination conditions, are extracted at each level of the image pyramid with uniform pattern, rotation invariance pattern and rotation invariance uniform pattern to generate an LBP pyramid. Thirdly, local binary patterns based on variance (LBPV) with the same patterns are also adopted in the same way to generate an LBPV pyramid. And fourthly, histograms of the LBP and LBPV pyramids are computed, and then all the histograms are concatenated into an enhanced feature vector. A neural network classifier is trained and used for discrimination of smoke and non-smoke objects. Experimental results show that the features are insensitive to rotation and illumination, and that the method is feasible and effective for video-based smoke detection at interactive frame rates. (C) 2011 Elsevier Ltd. All rights reserved.
