A Probabilistic Approach for Vision-Based Fire Detection in Videos

Abstract

Abstract-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.

Video fire detection – Review

“…Spatial color difference analysis [24,13,28,32] focuses on this characteristic. Using range filters [24], variance/histogram analysis [32], or spatial wavelet analysis [13,28], the spatial color variations in pixel values are analyzed to distinguish ordinary fire-colored objects from uncontrolled fires. In …”

“…Therefore disorder analysis of boundary contours of a moving object is useful for fire detection. Some examples of frequently used metrics are randomness of area size [23,32], boundary roughness [14,11,28,32], and boundary area disorder [18].…”

“…Other classification methods include the AdaBoost method [22], neural networks [29,35], Bayesian classifiers [30,32], Markov models [28,33] and rule-based classification [58].…”

Towards a solid solution of real-time fire and flame detection

“…Almost all fire detection methods use color as a distinct feature of fire, but explored it in different color space. Such as methods in [21][22] [4] used RGB color information, and method in [11] in used HSI color space. There are also other color spaces such as YUV and LAB.…”

“…Despite of the growing needs and interests in fire detection, there is still not a large number of work on fire detection in the computer vision literature [4]. Building a robust fire detection system is challenging in following two aspects: (1) fire or flame is flexible in shape and intensity, and it has no fixed structure or appearance.…”