Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm that is based on a saliency detection technique and on the uniform local binary pattern (ULBP). In still images and video sequences, an area that contains an open flame is always noticeable because fire is an exceptional event. Thus, to utilize the color information of flame pixels, the probability density function (pdf) of the flame pixel color can be obtained using Parzen window nonparametric estimation. This a priori pdf is then fused with the saliency detection phase as top-down information so that the flame candidate area can be extracted. To reduce the number of false alarms, the image texture of the candidate area is analyzed by ULBP, and an exponential function with two parameters is utilized to model the texture of the flame area. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while ensuring the accurate classification of positive samples. The classification performance of our proposed method is proven to be better than that of alternative algorithms.