Abstract: Wildfires are detected by controlling image scanning within the viewing range of a video camera to generate digital images that are analyzed to detect gray colored regions, and then to determine whether a detected gray colored region is smooth. Further analysis to determine movement in a gray colored smooth region uses a past image which is within a slow moving time range, as determined by a strategy for controlling the image scanning. Additional analysis connects a candidate region to a land portion of the image, and a support vector machine is applied to a covariance matrix of the candidate region to determine whether the region shows smoke from a wildfire.

Abstract: A video based method to detect volatile organic compounds (VOC) leaking out of components used in chemical processes in petrochemical refineries. Leaking VOC plume from a damaged component causes edges present in image frames to loose their sharpness, leading to a decrease in the high frequency content of the image. Analysis of image sequence frequency data from visible and infrared cameras enable detection of VOC plumes in real-time. Analysis techniques using adaptive background subtraction, sub-band analysis, threshold adaptation, and Markov modeling are described.