Machine vision-based real-time early flame and smoke detection

Abstract

This paper proposes a novel real-time machine video-based flame and smoke detection method that can be incorporated with a surveillance system for early alerts. Automatic monitoring systems use the motion history detection algorithm to register the possible flame and smoke position in a video and then analyze the spectral, spatial and temporal characteristics of the flame and smoke regions in the image sequences. The spectral probability density is represented by comparing the flame and smoke color histogram model, where HSI color spaces are used. The spatial probability density is represented by computing the flame and smoke turbulent phenomena with the relation of perimeter and area. Statistical distribution of the spectral and spatial probability density is weighted with the fuzzy reasoning system to give the potential flame and smoke candidate region. The temporal probability density is represented by extracting the flickering area with level crossing and separating the alias objects from the flame and smoke region. Then, the continuously adaptive mean shift (CAMSHIFT) vision tracking algorithm is employed to provide feedback of the flame and smoke real-time position at a high frame rate. Experimental results under a variety of conditions show that the proposed method is capable of detecting flame and smoke reliably.