A video smoke detection method based on structural similarity index determined by complexity of image

Abstract

Video-based smoke detection has a very wide range of applications. In this paper, we propose a smoke detection method based on the structural similarity and complexity of image. The background of image blurred by smoke causes the degradation of image quality, similar to adding noise to the image, which is very different from the background obscured by objects. Thus the structural similarity of image, usually for objective quality assessment of image, can be utilized to detect smoke qualitatively. The value of structural similarity index of image is affected by the complexity of image. We extract the texture features of image based on the gray level co-occurrence matrix and use the weighted sum of the second moment, the contrast, the inverse moment, the entropy and the correlation of image to determine the complexity of image. On this basis, we propose a method based on the structural similarity index of image determined by the image complexity for qualitative and quantitative smoke detection, and develop a DSPs system of video smoke detection based on the DM6437 EVM made by Texas Instruments. Experimental results show that the values of the structural similarity index determined by complexity of image are in good agreement with the results of the obscuration coefficient measured from optical smoke density meter.