A new approach to vision-based fire detection using statistical features and Bayes classifier

Computer vision - based fire detection has recently attracted a great deal of attention from the research community. In this paper, the authors propose and analyse a new approach for identifying fire in videos. In this approach, we propose a combined algorithm for detecting the fire in videos based on the changes of the statistical features in the fire regions between different frames. The statistical features consist of the average of the red, green and blue channel, the coarseness and the skewness of the red channel distribution. These features are evaluated, and then classified by Bayes classifier, and the final result is defined as fire-alarm rate for each frame. Experimental results demonstrate the effectiveness and robustness of the proposed method. © 2012 Springer-Verlag.