基于多维动态纹理分析的烟雾检测算法

Smoke Detection Algorithm Based on Multi-dimensional Dynamic Texture Analysis

为了提高动态纹理特征分析的可靠性且降低计算量,提出了一种基于多维动态纹理分析的烟雾检测算法。该算法在预处理阶段利用ICA烟雾前景初步分离烟雾模型得到初步烟雾前景,然后通过GBVS提取多通道、多尺度的底层特征得到烟雾前景显著区域,以提高前景目标检测阶段对烟雾前景的分割精确度;在烟雾特征提取阶段,提出基于多维特征分析的烟雾特征提取检测方法(h-LDS/RGBH),该方法首先经过烟雾颜色和背景差分预处理得到烟雾候选区域,然后在四维图像块中引入RGB和HOG特征,最后基于对多维图像数据的高阶分解,分析烟雾视频的动态特征。多维动态纹理分析(h-LDS/RGBH)改善了烟雾特征提取阶段所提取的烟雾特征稳定性不高且对烟雾的判断准则过于简单的缺点,提高了动态纹理特征分析的可靠性。实验表明,其检测率高于LDS和h-LDS/RGB的识别率。实验表明,该算法的检测率高于LDS和h-LDS/GRB.

Traditional video smoke detection algorithm only uses brightness values as image information when building a multi-dimensional image block,and has higher computation cost owing to the dense sampling.In order to improve the reliability of dynamic texture feature analysis and reduce the cost of computation,this paper puts forward a smoke detection algorithm based on multi-dimensional dynamic texture analysis.In the preprocessing stage,the preliminary separation of smoke foreground is performed by using ICA model,and then the smoke foreground region is found by extracting bottom features with multiple channels and multiple scales through GBVS,thus to improve the segmentation accuracy.In the phase of smoke feature extraction,a smoke feature extraction and detection method based on multi-dimensional feature analysis is proposed.First,the smoke candidate region is obtained through smoke color and background subtraction processing;then the RGB and HOG features are introduced in the four-dimensional image block;finally,the dynamic characteristics of smoke video are analyzed on the basis of the higher order decomposition of multidimensional image data.Owing to the sliding time window,the exact position and the specific time of smoke occurrence can be determined,therefore,the stability criterion of the smoke feature extraction phase and the reliability analysis of dynamic texture features are improved.Experiments show that the recognition rate of the proposed algorithm is higher than that of LDS and h-LDS/GRB.