Smoke Detection in Video Surveillance Using Optical Flow and Green’s Theorem

Finding smoke in surveillance videos can be crucial in early detection of fire emergencies. Such early detections improve damage prevention and control by enabling the authorities to take the necessary precautionary steps. This paper describes a smoke detection technique developed for videos taken in visual band. The method makes use of optical flow and color filtering to detect smoke covered regions and the associated smoke sources. Next it extracts dynamic smoke features such as average upwards motion above the source and divergence around the source via Green’s theorem. This determines whether the selected region contains smoke. In turn, the extracted dynamic characteristics of the smoke pattern greatly improve detection accuracy of the method and produce highly robust results as demonstrated in the experimental results.