Fire detection with video using fuzzy c-means and back-propagation neural network
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Fire flame detection in video sequences using multi-stage pattern recognition techniques
Engineering Applications of Artificial Intelligence
A new approach to vision-based fire detection using statistical features and bayes classifier
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Digital Signal Processing
Recursive Bayesian fire recognition using greedy margin-maximizing clustering
Machine Vision and Applications
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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.