基于多尺度LBP与GBP特征的火焰识别

为了提高大空间建筑场景中基于视频图像的火灾的识别率,提出了一种基于多尺度LBP与GBP纹理特征的火焰识别算法。首先在RGB颜色空间对连续数帧火灾图像进行预处理,并进行频闪特性分析以提取疑似火焰区域；建立疑似火焰图像高斯差分尺度空间,利用局部二值模式(LBP)和全局二值模式(GBP)提取火焰局部纹理特征和全局纹理特征；最后将多尺度的纹理特征输入到支持向量机进行识别。实验结果表明,LBP与GBP相结合的方法具有对光照不变的特性,获得了较好的火焰识别率。

Fire Image Detection Based on LBP and GBP Features with Multi-scales

In order to improve the fire detection rate based on video monitoring in large-span space buildings,this paper proposed a fire recognition method based on LBP and GBP features with multi-scales.Series flame of fire images were preprocessed in RGB space at first,and the flame candidate areas were located by the stroboscopic feature.We established the Gaussian difference scale space for fire images,and then LBP-feature and GBP-feature with different scales were extracted from these candidate areas.Finally,these features were put into SVM classification to recognize whether it is a flame.Experimental results show that the combination of LBP and GBP is invariant to uneven illumination,and improves the accurate of recognition flame.