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This paper proposes a vision-based fire and smoke segmentation system whichuse spatial, temporal and motion information to extract the desired regionsfrom the video frames. The fusion of information is done using multiplefeatures such as optical flow, divergence and intensity values. These featuresextracted from the images are used to segment the pixels into different classesin an unsupervised way. A comparative analysis is done by using multipleclustering algorithms for segmentation. Here the Markov Random Field performsmore accurately than other segmentation algorithms since it characterizes thespatial interactions of pixels using a finite number of parameters. It builds aprobabilistic image model that selects the most likely labeling using themaximum a posteriori (MAP) estimation. This unsupervised approach is tested onvarious images and achieves a frame-wise fire detection rate of 95.39%. Hencethis method can be used for early detection of fire in real-time and it can beincorporated into an indoor or outdoor surveillance system.

Unsupervised Segmentation of Fire and Smoke from Infra-Red Videos