Automated fire detection is an active research topic in computer vision. In this paper, it proposes vision based approach for identifying fire in videos. Computer vision-based fire detection algorithms are usually applied in closed-circuit television surveillance scenarios with controlled background. This method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content. In the latter case, there are large variations in fire and background characteristics depending on the video instance. This method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these features are powerful discriminants. The behavioral change of each one of these features is evaluated, and the results are then combined according to the Bayes classifier for robust fire recognition. A priori knowledge of fire events captured in videos is used to significantly improve the classification results. In this paper, the spatial temporal feature based from selecting methods to reduce the computation time.

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Fire Detection Pattern Recognition Potential Fire Mask Temporal Changes.