An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems

Abstract

This paper proposes an effective, four-stage smoke-detection algorithm using video images. In the first stage, an approximate median method is used to segment moving regions in a video frame. In the second stage, a fuzzy c-means (FCM) method is used to cluster candidate smoke regions from these moving regions. In the third phase, a parameter extraction method is used to extract a set of parameters from spatial and temporal characteristics of the candidate smoke regions; these parameters include the motion vector, surface roughness and area randomness of smoke. In the fourth stage, the parameters extracted from the third stage are used as input feature vectors to train a support vector machine (SVM) classifier, which is then used by the smoke alarm to make a decision. Experimental results show that the proposed four-stage smoke-detection algorithm outperforms conventional smoke-detection algorithms in terms of accuracy of smoke detection, providing a low false-alarm rate and high reliability in open and large spaces.