A new approach for smoke detection with texture analysis and support vector machine

Early and certain fire detection is one of the important issues to keep safe infrastructures. Especially, it becomes an urgent problem in large facilities like port facilities, large factories and power plants, due to its large harmful effect to surrounding areas. In these places, to detect the fire or flame directly is have some difficulties because they are open and hence have problem to set sensor devices. So smoke is an important and useful sign to detect the fire robustly even in such open areas. In this study, we propose a novel and robust smoke detection method based on image information. Smoke is consider a moving objects, so firstly we extract moving objects of input images as smoke candidate regions. Because smoke has a characteristic pattern as image information, we treat smoke patterns as textures. Here we use texture analysis to extract feature vectors of images. To classify extracted moving objects are smoke or non-smoke, we use support vector machine (SVM) with texture features as input features. Extraction of moving objects are sometimes easily affected by environmental conditions. So we accumulate the result of the classification with SVM about time to obtain accurate extraction results of smoke regions.