FMF的火焰显著性检测

为准确定位火源点,实现火灾预警,提出一种基于人眼视觉注意机制的实时监测火灾预警方法。首先,根据图像对抗理论,提取视频序列中每一帧图像的亮度和颜色特征;其次,运用像素级显著性检测算法,构建描述特征信息的多尺度空间高斯金字塔;然后,运用跨尺度特征相加方法,融合中心-邻域对比度金字塔,得到静态显著性图;最后,结合动态帧差法,将多特征融合(FMF)算法得到的显著性图作动态帧差,寻找视频帧中属于火焰的区域,在公开的数据集上就4种评价指标与6种代表性算法作对比。结果表明:FMF算法通过显著性分析方法描述多尺度空间特征信息,其鲁棒性更强;与6种算法相比,FMF算法在准确率和漏检率上有较明显的优势,且能准确识别与定位火焰,防范火灾的发生。

Flame saliency detection based on FMF

To accurately locate the fire source and achieve early warning of fire, a real time fire warning monitoring method based on human visual attention mechanism was developed. Firstly, the brightness and color features of each frame of the video sequence were extracted according to image opponent theory. Secondly, pixel-level saliency detection algorithm was applied to construct a multi-scale spatial Gaussian pyramid which describes feature information. Then, the static saliency map was generated by merging centerneighbor contrast pyramid through the cross-scale feature addition method. Finally, the saliency map obtained from FMF algorithm was used as the dynamic frame difference based on the dynamic frame difference method to find the region of flame on video frames, and the proposed approach was compared with 6 representative algorithms in terms of 4 performance criteria on public datasets. The results show that FMF algorithm demonstrates stronger robustness in describing multi-scale spatial feature information through the saliency analysis method, and with obvious advantages in accuracy and missed rate compared with other algorithms, it can accurately identify and locate the flame so as to prevent the occurrence of fire accidents.