Smoke detection in open areas using its texture features and time series properties

In extensive facilities such as port facilities, chemical plants, and power stations, it is important to detect a fire early and certainly. The purpose of this paper is to present a new smoke detection method in open areas, as smoke is considered as a significant signal of the fire. It is assumed that the camera monitoring the scene of the open area is stationary. Since smoke does not keep stationary shape or image features like edges, it is difficult apply ordinal image processing techniques such as the edge or contour detection directly. In this paper, we propose a novel method of the smoke detection in an image sequence, in which we combines the several images techniques to detect smoke. We apply it to images of open areas under general environmental conditions. First, moving objects are detected from gray scale image sequences, and then the noise is removed with the image binarization and the morphological operation. Furthermore, since the smoke pattern must be examined, the smoke feature is extracted with the texture analysis. Then, to obtain the final result of the proposed method, we discussed the properties of the proposed features as the time series data.