Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks

This paper proposes a new vision-based early fire detection method for real-world application. First, candidate fire regions are detected using a background and color model of fire. Probabilistic models of the fire are then generated based on the fact that fire pixel values in consecutive frames change constantly. These models are then applied to Bayesian Networks. This paper uses hierarchical Bayesian Networks that contain intermediate nodes. Four probability density functions for evidence at each node are used. The probability density functions for each node are modeled using the skewness of the color red, and three high frequencies obtained from a wavelet transform. The proposed system was successfully applied to various fire-detection tasks in real-world environments, and it effectively distinguished fire from fire-colored moving objects.