Smoke detection in open areas with texture analysis and support vector machines

Early and certain fire detection is one of the important issues to keep infrastructures safe. Especially, it becomes an urgent problem for open places such as port facilities, large factories, and power plants, due to its large harmful effect to the surrounding areas. In these places, direct detection of fire or flame has some difficulties because they are open and hence have problem to set sensor devices. Therefore, smoke is an important and useful sign to detect fire or flames robustly even in such cases. In this paper, we present a novel smoke detection method based on image information. First, we extract moving objects in an image sequence as smoke candidate regions in a preprocessing step. Since smoke has a characteristic pattern as image information, we focus on the texture pattern of smoke. Here, we use texture analysis to extract feature vectors of the images. To classify extracted areas of moving objects to smoke or nonsmoke, we use support vector machines (SVMs) with texture features as an input feature vector. Extraction of moving objects is sometimes easily and greatly affected by environmental conditions such as wind, background objects, and so forth. It obviously causes bad classification results. To solve this problem, we additionally accumulate the results of classification with SVM about time to obtain accurate extraction result of smoke regions under these conditions. Experimental results using real-scene data show that our method works effectively under several different environmental conditions. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.