Indoor video-based smoke detection

Abstract: Traditional smoke detection methods need sensors close to the source of the smoke. In order to avoid this disadvantage, this thesis presents a method for indoor video based smoke detection. In addition, it helps to improve the success rate of detection, as well as reducing the false detection rate of suspected smoke. This method consists of two parts. First, we create a Gaussian mixture model to detect smoke color pixels. Then, we use the smoke’s dynamic features to detect the smoke area and extract it from the area found in the first step, and in that way we can find the real smoke area. Our results show that this method can detect the smoke even before the flame appears. This will provide earlier warnings of fire, thus reducing economic losses and casualties.