基于颜色模型和稀疏表示的图像型火焰探测

常用的图像型火焰探测算法是提取火焰在图像上表现出的单个特征信息或其有效组合作为识别的依据,需要大量的训练样本进行学习与参量优化,且识别率对特征选择的要求也很高.本文从火焰的整体特征考虑,提出了基于颜色模型和稀疏表示模型相结合的图像型火灾探测方法.首先在HIS空间建立颜色模型对火灾图像进行预处理提取出疑似区域,建立稀疏表示模型,并利用主成分分析方法构造火焰和疑似火焰物体的特征字典,最后利用l1-minimization计算测试样本与训练样本的最小逼近残差实现火焰和干扰物体的分类识别.实验结果表明,该方法提高了火灾图像的分类准确度和识别速度,同时具有较高的准确率.

Image Fire Detection Based on Color Model and Sparse Representation

 Some single feature information or their effective combinations which fire flame behaves are extracted as the basis of image fire flame recogniton in common algorithms.And large number of training samples are needed for learning procedure and parameters optimization.Moreover the recognition rate depends on the selection of features.Considering the global feature of fire flame,an algorithm was proposed based on color model and sparse representation for fire detection.Firstly,the regions with fire-like colors were roughly separated by color modeling in the space of HIS.Secondly,sparse representation model was built,and then the codebook of flames and suspected objects were constructed using PCA.Finally,the classification of fire flames and disturbances was implemented by calculating the minimum approximation residual error between testing samples and training samples using l1-minimization.The experiment results show that the algorithm can effectively improve the classification precision and recognition speed,and also it achieves higher accuracy.