A Probabilistic Approach for Vision-Based Fire Detection in Videos

• Published in: IEEE transactions on circuits and systems for video technology 2010-05, Vol.20 (5), p.721-731
• Main Authors: Borges, Paulo Vinicius Koerich, Izquierdo, Ebroul
• Format: Article
• Language: English
• Subjects: Active control; Algorithms; Applied sciences; Artificial intelligence; Automatic control; Classification; Computer science; control theory; systems; Computer vision; Content based retrieval; Detection algorithms; Exact sciences and technology; Eyes & eyesight; Fire detection; Fires; Image processing; Information retrieval; Information theory; Information, signal and communications theory; Mathematical models; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; probabilistic pattern recognition; Roughness; Signal processing; Spatial databases; Studies; Surveillance; Telecommunications and information theory; video processing; Videos
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2010.2045813

Automated fire detection is an active research topic in computer vision. In this paper, we propose and analyze a new method for identifying fire in videos. Computer vision-based fire detection algorithms are usually applied in closed-circuit television surveillance scenarios with controlled 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. In the latter case, there are large variations in fire and background characteristics depending on the video instance. 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. In addition, a priori knowledge of fire events captured in videos is used to significantly improve the classification results. For edited newscast videos, the fire region is usually located in the center of the frames. This fact is used to model the probability of occurrence of fire as a function of the position. Experiments illustrated the applicability of the method.