基于多特征融合的交通标志分类
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摘要
为有效提高交通标志分类的准确度,提出一种融合全局特征和局部特征的多特征交通标志分类方法。首先提取能够描述标志图像内部纹理信息的局部二值模式(local binary pattern,LBP)特征,再提取能够表示标志图像形状信息的方向梯度直方图(histogram of oriented gradient,HOG)特征和描述图像粗略轮廓信息的全局Gist特征,然后采用线性组合方式,实现特征融合互补,并通过主成分分析(principal components analysis,PCA)法进行数据降维,最后采用支持向量机(support vector machine,SVM)分类器进行交通标志训练与识别。试验结果表明:相对于单一特征的交通标志分类方法,基于多特征融合的算法获得了更高的分类精确度,同时也满足实时性要求。
In order to effectively improve the accuracy of the traffic sign classification,a newmethod was proposed through fusing the global and local features. First,local binary pattern( LBP) feature was extracted which could describe the internal texture information of traffic sign image,and then histogram of oriented gradient( HOG) feature which could represent shape information and global gist feature with description of the rough outline of the image information were extracted,and then linear combination was used to achieve feature complementary. The principal component analysis( PCA) was used for data dimensionality reduction. Final traffic sign training and classification was carried out using support vector machine( SVM) classifier. The experiments showed that with respect to a single feature extraction classification of traffic signs,the algorithm based on multi-featured fusion achieveed higher classification accuracy,but also met real-time requirements.
In order to effectively improve the accuracy of the traffic sign classification,a newmethod was proposed through fusing the global and local features. First,local binary pattern( LBP) feature was extracted which could describe the internal texture information of traffic sign image,and then histogram of oriented gradient( HOG) feature which could represent shape information and global gist feature with description of the rough outline of the image information were extracted,and then linear combination was used to achieve feature complementary. The principal component analysis( PCA) was used for data dimensionality reduction. Final traffic sign training and classification was carried out using support vector machine( SVM) classifier. The experiments showed that with respect to a single feature extraction classification of traffic signs,the algorithm based on multi-featured fusion achieveed higher classification accuracy,but also met real-time requirements.
引文
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[2]孙光民,王晶,于光宇,等.自然背景中交通标志的检测与识别[J].北京工业大学学报,2010,36(10):1337-1343.SUN Guangmin,WANG Jing,YU Guangyu.The detection and recognition of traffic sign in natural scenes[J].Journal of Beijing University of Technology,2010,36(10):1337-1343.
[3]GREENHALGH J,MIRMEHDI M.Real-time detection and recognition of road traffic signs[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1498-1506.
[4]刘华平,李建民,胡晓林,等.动态场景下的交通标识检测与识别研究进展[J].中国图象图形学报,2013,18(5):493-503.LIU Huaping,LI Jianmin,HU Xiaolin,et al.Recent progress in detection and recognition of the traffic signs in dynamic scenes[J].Journal of Image and Graphics,2013,18(5):493-503.
[5]谷明琴,蔡自兴,李仪,等.基于多模型表示的交通标志识别算法设计[J].控制与决策,2013,28(6):844-848.GU M ingqin,CAI Zixing,LI Yi,et al.Traffic sign recognition algorithm design based on multi-modal representation[J].Control and Decision,2013,28(6):844-848.
[6]LIU C S,CHANG F L,CHEN Z X.Rapid multiclass traffic sign detection in high-resolution images[J].IEEE Transactions on Intelligent Transportation Systems,2014,15(6):2394-2403.
[7]MOGELMOSE A,TRIVEDI M M,MOESLUND T B.Vision-based traffic sign detection and analysis for intelligent driver assistance systems:perspectives and survey[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1484-1479.
[8]张静,何明一,戴玉超,等.多特征融合的圆形交通标志检测[J].模式识别与人工智能,2011,24(2):226-232.ZHANG Jing,HE M ingyi,DAI Yuchao,et al.M ulti-feature fusion based circular traffic sign detection[J].PR&AI,2011,24(2):226-232.
[9]KHAN J F,BHUIYAN S M A,ADHAMI R R.Image segmentation and shape analysis for road-sign detection[J].IEEE Transactions on Intelligent Transportation Systems,2011,12(1):83-96.
[10]CIRESAN D,MEIER U,MASCI J,et al.Multi-column deep neural network for traffic sign classification[J].Neural Netw orks,2012,32(8):333-338.
[11]ZAKLOUTA F,STANCIULESCU B.Real-time traffic sign recognition in three stages[J].Robotics and Autonomous Systems,2014,62(1):16-24.
[12]曹红根,袁宝华,朱辉生.结合对比度信息与LBP的分块人脸识别[J].山东大学学报(工学版),2012,42(4):29-34.CAO Honggen,YUAN Baohua,ZHU Huisheng.Recognition of intersected face based on contrast information and local binary pattern[J].Journal of Shandong University(Engineering Science),2012,42(4):29-34.
[13]刘威,段成伟,遇冰,等.基于后验HOG特征的多姿态行人检测[J].电子学报,2015,43(2):217-224.LIU Wei,DUAN Chengw ei,YU Bing,et al.M ulti-pose pedestrian detection based on posterior HOG feature[J].Acta Electronica Sinica,2015,43(2):217-224.
[14]OLIVA A,TORRALBA A.Modeling the shape of the scene:a holistic representation of the spatial envelope[J].International Journal of Computer Vision,2001,42(3):145-175.
[15]OLIVA A,TORRALBA A.Building the Gist of a scene:the role of global image features in recognition[J].Progress in Brain Research:Visual Perception,2006,155(2):23-36.
[16]杨昭,高隽,谢昭,等.局部Gist特征匹配核的场景分类[J].中国图象图形学报,2013,18(3):264-270.YANG Zhao,GAO Jun,XIE Zhao,et al.Scene categorization of local Gist feature match kernel[J].Journal of Image and Graphics,2013,18(3):264-270.
[17]孙伟,钟映春,谭志,等.多特征融合的室内场景分类研究[J].广东工业大学学报,2015,32(1):75-79.SUN Wei,ZHONG Yingchun,TAN Zhi,et al.Research on multi-featured fusion for indoor scene recognition[J].Journal of Guangdong University of Technology,2015,32(1):75-79.
[18]WOLD S,ESBENSEN K,GELADI P.Principal component analysis[J].Chemometrics and Intelligent Laboratory Systems,1987,2(1):37-52.
[19]OJALA T,PIETIKAINEN M,MAENPAA T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis&M achine Intelligence,2002,24(7):971-987.
[20]DALAL N,TRIGGS B.Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition,International Conference on.Beijing,China:IEEE,2005:886-893.
[21]李晓宇,张新峰,沈兰荪.支持向量机(SVM)的研究进展[J].测控技术,2006,25(5):7-12.LI Xiaoyu,ZHANG Xinfeng,SHEN Lansun.Some developments on support vector machine[J].M easurement&Control Technology,2006,25(5):7-12.
[22]HASTIE T,TIBSHIRANI R.Discriminant adaptive nearest neighbor classification[J].IEEE Transactions on Pattern Analysis&M achine Intelligence,1996,18(6):607-616.
[23]FREUND Y,SCHIPARE RE.Experiments with a new boosting algorithm[C]//Thirteenth International Conference on Machine Learning.Bari,Italy:Universita'di Bari,1996:148-156.