基于PoolNet显著性和SURF-VIBE模型的林火视频烟雾提取算法

Smoke Extraction Algorithm for Forest Fire in Video Based on PooLNet Saliency and SURF-VIBE Model

烟雾的实时视频探测可用于早期森林火灾的预警,然而,由于烟雾具有飘动、扩散、闪烁等特性,通过实时视频提取烟雾区域具有极大挑战。本文根据人眼视觉注意力机制,将阴燃烟雾看作视频中湍流和灰色显著的区域,提出了一种基于显著性检测和SURF-VIBE模型的疑似烟雾区域提取算法。首先采用一种基于PoolNet的显著性检测方法获得烟雾显著性图谱,通过VIBE运动检测算法获得视频中运动前景,并使用SURF特征匹配算法消除相机抖动等对运动前景带来的干扰,再由计算出的运动前景构造运动能量函数,对显著性谱进行估计,最终提取出疑似烟雾区域。实验验证结果表明:该算法检测准确率为91.3%,每帧检测速度为0.028 s,可用于实时视频烟雾探测。

Real-time video detection of smoke can be used for early warning of forest fires,however,extracting smoke areas through real-time video has great challenges,since smoke has the characteristics of fluttering,spreading,and flickering.In this paper,a suspicious smoke area extraction algorithm based on saliency detection and SURF-VIBE model is proposed,which smoldering smoke is regarded as a turbulent and gray area in the video according to the human visual attention mechanism.Firstly,a saliency detection method based on PoolNet is used to obtain a smoke saliency map.The motion foreground is obtained through the VIBE motion detection algorithm.Then,the SURF feature matching algorithm is used to eliminate the interference caused by camera shake and other motion foreground,and then calculated by motion foreground to construct the motion energy function,estimate the significance spectrum,and finally extract the suspected smoke area.Experimental results show that the detection accuracy of the algorithm can reach 91.3%,and the detection speed per frame can reach 0.028 seconds,which is suitable for real-time video smoke detection.