Real-time video-based fire smoke detection system

Abstract

A real-time video-based fire smoke detection method that can be incorporated with a automatic monitoring system for early alerts is proposed by this paper. The successive processing steps of our real-time algorithm are using the motion history segmentation algorithm to register the possible fire smoke position in a video and then analyze the spectral, spatial and temporal characteristics of the fire smoke regions in the image sequences. The spectral probability density is represented by comparing the fire smoke color histogram model, where HSI color spaces are used. The spatial probability density is represented by computing the fire 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 fire smoke candidate region. The temporal probability density is represented by extracting the flickering area with level crossing and separating the alias objects from the fire smoke region. Then, the continuously adaptive mean shift (CAMSHIFT) vision tracking algorithm is employed to provide feedback of the fire smoke real-time position at a high frame rate. Experimental results in a variety of conditions show the proposed method is capable of detecting fire smoke reliably.