Notice of Violation of IEEE Publication PrinciplesEffective visual fire detection in video sequences using probabilistic approach

The objective is to develop a probabilistic approach for vision-based fire detection in videos. The proposed 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. Temporal matching concept is used to reduce the computational complexity and also to allow fast processing of videos. Early vision-based fire detection techniques target surveillance applications with static cameras and consequently reasonably controlled or static background. In contrast, the proposed method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content.