Deep Belief Network For Smoke Detection

Forest fire is an serious hazard in many places around the world. For such threats, video-based smoke detection would be particularly important for early warning because smoke arises in any forest fire and can be seen from a long distance. This paper presents a novel and robust approach for smoke detection that employs Deep Belief Networks. The proposed method is divided into three phases. In the preprocessing phase, the region of high motion is extracted by background subtraction method. During the next phase, smoke pixel intensities are extracted from the Red, Green and Blue and Luminance; Chroma:Blue; Chroma:Red color spaces for foreground regions. Subsequently, second feature which is based on texture is computed for detecting smoke regions in which Local Extrema Co-occurrence Pattern, an improved version of local binary patterns are extracted from different foreground regions which compute not only texture of smoke but also intensity and color of smoke using Hue Saturation Value color space. Finally, Deep Belief Network is employed for classification. The proposed method proves its accuracy and robustness when tested on different varieties of scenarios whether wildfire-smoke video, hill base smoke video, indoor or outdoor smoke videos.