Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition

In this paper, we propose a novel method for video-based smoke detection, which aims to discriminate smoke from smoke-colored moving objects by applying spatio-temporal analysis, smoke motion modeling and dynamic texture recognition. Initially, candidate smoke regions in a frame are identified using background subtraction and color analysis based on the HSV model. Subsequently, spatio-temporal smoke modeling consisting of spatial energy analysis and spatio-temporal energy analysis is applied in the candidate regions. In addition, histograms of oriented gradients and optical flows (HOGHOFs) are computed to take into account both appearance and motion information, while dynamic texture recognition is applied in each candidate region using linear dynamical systems and a bag of systems approach. Dynamic score combination by mean value is finally used to determine whether there is smoke or not in each candidate image region. Experimental results presented in the paper show the great potential of the proposed approach.