IMAGE BASED SMOKE DETECTION WITH LOCAL HURST EXPONENT

Smoke is an important sign for early fire detection. Image based detection ethods are more useful than other methods which use some special sensor devices. When treating image information of smoke, it is important to consider characteristics of smoke. In this study, we consider that the image information of smoke is a self-affine fractal. We focus on the nature of smoke and present a new smoke detection method based on the fractal property of smoke. We use the Hurst exponent H, which is one of the widely known exponent of fractals. We calculate H of smoke from a relation between H and the wavelet transform of the image. So we detect smoke areas in images with H through the wavelet transform. Moreover, to obtain the accurate detection result, we use the time-accumulation technique to smoke detection results of each image. In experiments, we show the effectiveness of our method with the fractal property of smoke.