基于GMM与三维LBP纹理的视频火焰检测
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摘要
针对候选区域提取准确度问题及火焰特征的描述能力,提出一种基于高斯混合模型(Gaussian mixture model,GMM)与三维的局部二值模式(local binary pattern,LBP)纹理特征的火焰检测算法,分析火焰在RGB与HSV两个空间中的分布规律,训练出火焰的高斯混合模型,提取火焰候选区域。重点研究火焰的纹理特征,将LBP纹理与火焰的运动特征相结合形成一种新的三维LBP纹理,提高纹理特征对火焰的分类效果。使用单分类支持向量机(one-class support vector machine,One-classSVM)分类方法,判定候选区域是否为火焰。
In order to solve the problems of extracting the accuracy of the candidate region and improve the description ability of the flame characteristics,a novel flame detection algorithm based on Gaussian mixture model(GMM) and three-dimensional locality binary pattern(LBP) texture features was proposed.The distribution of flame was analyzed in two spaces of RGB and HSV,and the GMM was trained to extract the flame candidate region.The texture characteristics of the flame was selected as an important feature.The original LBP texture was fused with the motion characteristics of the flame to form a new three-dimensional LBP texture to improve the classification effect of the texture feature on the flame.The one-class support vector machine(One-Class SVM) classification method was used to determine whether the candidate area was a flame.
In order to solve the problems of extracting the accuracy of the candidate region and improve the description ability of the flame characteristics,a novel flame detection algorithm based on Gaussian mixture model(GMM) and three-dimensional locality binary pattern(LBP) texture features was proposed.The distribution of flame was analyzed in two spaces of RGB and HSV,and the GMM was trained to extract the flame candidate region.The texture characteristics of the flame was selected as an important feature.The original LBP texture was fused with the motion characteristics of the flame to form a new three-dimensional LBP texture to improve the classification effect of the texture feature on the flame.The one-class support vector machine(One-Class SVM) classification method was used to determine whether the candidate area was a flame.
引文
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[2]KONG S G,JIN Donglin,LI Shengzhe,et al.Fast fire flame detection in surveillance video using logistic regression and temporal smoothing[J].Fire Safety Journal,2016,79:37-43.
[3]JIANG Bo,LU Yongyi,LI Xiying,et al.Towards a solid solution of real-time fire and flame detection[J].Multimedia Tools and Applications,2015,74(3):689-705.
[4]吴建胜,高云玲,张宾.一种基于视频的火焰检测方法[J].计算机应用与软件,2014,31(1):173-175,190.WU Jiansheng,GAO Yunling,ZHANG Bin.A video-based flame detection method[J].Computer Applications and Software,2014,31(1):173-175,190.
[5]DIMITROPOULOS K,BARMPOUTIS P,GRAMMALIDIS N.Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(2):339-351.
[6]GANESAN P,RAJINI V,SATHISH B S,et al.HSV color space based segmentation of region of interest in satellite images[C]//Proceedings of the 2014 International Conference on Control,Instrumentation,Communication and Computational Technologies(ICCICCT).Kanyakumari,India:IEEE,2014,12(10):101-105.
[7]乔少杰,金琨,韩楠,等.一种基于高斯混合模型的轨迹预测算法[J].软件学报,2015,26(5):1048-1063.QIAO Shaojie,JIN Kun,HAN Nan,et al.Trajectory prediction algorithm based on Gaussian mixture model[J].Journal of Software,2015,26(5):1048-1063.
[8]唐岩岩,严云洋,刘以安.应用GMM的快速火焰检测[J].计算机科学,2012,39(11):283-285.TANG Yanyan,YAN Yunyang,LIU Yian.Fast flame detection with GMM[J].Computer Science,2012,39(11):283-285.
[9]DOSHI N P,SCHAEFER G,HOSSAIN S.Rotation invariant compound LBP texture features[C]//Proceedings of the 5th International Conference on Informatics,Electronics and Vision(ICIEV).Dhaka,Bangladesh:IEEE,2016:1057-1061.
[10]KUMAR A R,SARAVANAN D.Content based image retrieval using color histogram[J].Int.J.Comput.Sci.Inform.Technol,2013,4:242-245.
[11]王洪波.单分类支持向量机的学习方法研究[D].苏州:浙江大学,2012.WANG Hongbo.Research of learning method of single-class support vector machine[D].Suzhou:Zhejiang University,2012.
[12]GU B,SHENG V S,TAY K Y,et al.Incremental support vector learning for ordinal regression[J].IEEE Transactions on Neural Networks and Learning Systems,2015,26(7):1403-1416.
[13]CHEN T H,WU P H,CHIOU Y C.An early fire-detection method based on image processing[C]//Proceedings of the 2004International Conference on Image Processing.Singapore City,Singapore:IEEE,2004,3:1707-1710.
[14]TUNG X T,KIM J M.Fire flame detection in video sequences using multi-stage pattern recognition techniques[J].Engineering Applications of Artificial Intelligence,2012,25(7):1365-1372.
[15]BORGES P V K,IZQUIERDO E.A probabilistic approach for vision-based fire detection in videos[J].IEEE Transactions on Circuits and Systems for Video Technology,2010,20(5):721-731.(:)