摘要
针对现有视频图像火焰检测算法前景提取不完整、准确率低和误检率高等问题,提出一种基于改进混合高斯模型(GMM)和多特征融合的视频火焰检测算法。首先针对背景建模,提出了自适应高斯分布数和学习率的改进GMM方法,以提高前景提取效果和算法实时性;然后利用火焰颜色特征筛选出疑似火焰区域,再通过融合改进局部二值模式纹理和边缘相似度特征用于火焰检测。基于支持向量机设计火焰融合特征分类器并进行对比实验,在公开数据集上的实验结果表明,所提算法有效提高了背景建模效果,火焰检测准确率可达到92.26%,误检率低至2.43%。
Aiming at the problems of incomplete foreground extraction,low accuracy,and high false detection rate of the existing video image flame detection algorithms,a video flame detection algorithm based on improved Gaussian mixture model(GMM)and multi-feature fusion was proposed.Firstly,for background modeling,an improved GMM method with adaptive Gaussian distribution number and learning rate was proposed to improve the foreground extraction effect and algorithm real-time performance.Then the flame color characteristics were used to filter out the suspected flame regions,and local binary pattern texture and edge similarity features were used for flame detection.Based on support vector machine,a flame fusion feature classifier was designed and compared.Experimental results on the public datasets show that the algorithm proposed in this paper effectively improved the background modeling effect.The flame detection accuracy reached 92.26%,and the false detection rate was as low as 2.43%.
作者
张驰
孟庆浩
井涛
Zhang Chi;Meng Qinghao;Jing Tao(Institute of Robotics and Autonomous Systems,Tianjin Key Laboratory of Process Detection and Control,School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第4期128-137,共10页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61573252)
国家重点研发计划项目(2017YFC0306200)。
关键词
图像处理
火焰检测
视频图像
混合高斯模型
多特征融合
支持向量机
image processing
flame detection
video image
Gaussian mixture model
multi-feature fusion
support vector machines
作者简介
井涛,E-mail:jingtao@tju.edu.cn。
