Low false reject rate and false accept rate multi-step fire detection method

Abstract

This paper proposes an efficient fire detection method for intelligent monitoring of self-service banks. This method includes the following steps: motion region detection, color-based fire detection and fire region refinement. These steps are as follows. First, the method uses four Gaussian distributions to construct an adaptive background model of the scene, and exploits this model to obtain the moving regions of the image. Second, the method uses RGB color and HSI color to determine whether the moving regions are fire candidates or not. These two steps are able to detect almost all of the true fire regions but it also takes a portion of non-fire regions as the fire. Third, the proposed frame difference procedure and the distance of two fire regions of two adjacent frames to eliminate the false fire regions obtained using the first two steps. The experimental results show that the proposed method performs very well in small-scale fire detection. The false reject rate (FRR) and false accept rate (FAR) of our method are 10.4%, and 6.02%, respectively.