Smoke and flame detection in video sequences based on static and dynamic features

In this paper we propose efficient smoke and flame detection algorithms for intelligent video surveillance systems. Our algorithms consider dynamic and static features of smoke and flame: contrast, color, texture features and motion. For smoke detection the approach uses motion and contrast as the two key features of smoke. Motion is a primary sign and is used at the beginning for extraction from a current frame of candidate areas. In addition to consider a direction of smoke distribution the movement estimation based on the optical flow is applied. For flame detection we use color image segmentation, temporal and spatial wavelet analyses on the first step. After that color and texture features for candidate flame regions are extracted. Texture features are defined based on normalized gray level co-occurrence matrix after computation of local binary pattern. Experimental results are presented in the paper.