基于显著图与傅里叶描述子的交通标志检测
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摘要
在交通标志检测过程中,存在标志尺寸变化、旋转失真、投影失真以及部分被遮挡等问题。为此,提出一种基于显著图和傅里叶描述子的交通标志检测算法。采用频率调谐方法得到显著图并将其二值化,初步定位交通标志区域。通过提取区域外层轮廓,利用轮廓周长和长宽比特征滤除干扰信息,对合格轮廓进行凸壳处理,得到归一化凸壳傅里叶描述子,并与标准数据对比得到检测结果。实验结果表明,该算法检测率可达95%以上,同时满足交通标志检测的实时性要求。
Aiming at the problems in traffic sign detection process,such as that traffic sign shows dimensional changes,rotation distortion,projection distortion or the sign is partially occluded,in this paper,a traffic sign detection algorithm based on saliency map and Fourier Descriptor(FD) is proposed.Firstly,the Frequency Tuning(FT) method is used to get the saliency map.Secondly,binary operation is utilized on saliency map to achieve binary image and get the regions of traffic sign.By extracting outer contour of the regions,features of contour length and aspect ratio are utilized to filter interference information.Thirdly,the convex hull processing is applied for the eligible contours and FDs of convex hull are extracted and normalized.Finally,detection results are obtained according to the comparison with standard data.Experimental results demonstrate that the detection rate of the proposed algorithm is more than 95%.This algorithm meets the real-time performance requirement of traffic sign detection.
Aiming at the problems in traffic sign detection process,such as that traffic sign shows dimensional changes,rotation distortion,projection distortion or the sign is partially occluded,in this paper,a traffic sign detection algorithm based on saliency map and Fourier Descriptor(FD) is proposed.Firstly,the Frequency Tuning(FT) method is used to get the saliency map.Secondly,binary operation is utilized on saliency map to achieve binary image and get the regions of traffic sign.By extracting outer contour of the regions,features of contour length and aspect ratio are utilized to filter interference information.Thirdly,the convex hull processing is applied for the eligible contours and FDs of convex hull are extracted and normalized.Finally,detection results are obtained according to the comparison with standard data.Experimental results demonstrate that the detection rate of the proposed algorithm is more than 95%.This algorithm meets the real-time performance requirement of traffic sign detection.
引文
[1]邓雄伟.自然环境下道路交通标志的检测与识别研究[D].南京:南京理工大学,2014.
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[14]张志佳,李文强,张丹,等.基于颜色与形状特征的交通标志检测方法[J].计算机技术与发展,2015,25(7):174-178.
[15]Graham R L.An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set[J].Information Processing Letters,1972,1(4):132-133.
[16]周培德,付梦印.寻求简单多边形凸壳的线性时间算法[J].计算机工程与科学,2002,24(3):1-2,44.
[17]王涛,刘文印,孙家广,等.傅里叶描述子识别物体的形状[J].计算机研究与发展,2002,39(12):1714-1719.
[18]Houben S,Stallkamp J,Salmen J,et al.Detection of Traffic Signs in Real-w orld Images:The German Traffic Sign Detection Benchmark[C]//Proceedings of International Joint Conference on Neural Netw orks.Washington D.C.,USA:IEEE Press,2013:1-8.
[19]Belaroussi R,Tarel J P.Angle Vertex and Bisector Geometric Model for Triangular Road Sign Detection[C]//Proceedings of 2009 Workshop on Applications of Computer Vision.Washington D.C.,USA:IEEE Press,2009:577-583.
[20]Brkic'K,Pinz A,egvic'S.Traffic Sign Detection as a Component of an Automated Traffic Infrastructure Inventory System[C]//Proceedings of Annual Workshop of the Austrian Association for Pattern Recognition.Stainz,Austria:[s.n.],2009:1-12.
[2]Zhang Min,Liang Huawei,Wang Zhiling,et al.Real-time Traffic Sign Detection and Recognition for Intelligent Vehicle[C]//Proceedings of IEEE International Conference on M echatronics and Automation.Washington D.C.,USA:IEEE Press,2014:1125-1131.
[3]Cai Zixing,Gu Mingqin.Traffic Sign Recognition Algorithm Based on Shape Signature and Dual-tree Complex Wavelet Transform[J].Journal of Central South University,2013,20(2):433-439.
[4]Saponara S.Real-time Color/Shape-based Traffic Signs Acquisition and Recognition System[J].Proceedings of the SPIE,2013,8656:759-767.
[5]Lopez L D,Fuentes O.Color-based Road Sign Detection and Tracking[C]//Proceedings of International Conference on Image Analysis and Recognition.Berlin,Germany:Springer-Verlag,2007:1138-1147.
[6]Houben S.A Single Target Voting Scheme for Traffic Sign Detection[C]//Proceedings of IEEE Intelligent Vehicles Symposium.Washington D.C.,USA:IEEE Press,2011:124-129.
[7]Liang Ming,Yuan Mingyi,Hu Xiaolin,et al.Traffic Sign Detection by ROI Extraction and Histogram Features-based Recognition[C]//Proceedings of International Joint Conference on Neural Networks.Washington D.C.,USA:IEEE Press,2013:1-8.
[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.
[9]Chen Long,Li Qingquan,Li Ming,et al.Traffic Sign Detection and Recognition for Intelligent Vehicle[C]//Proceedings of the IEEE Intelligent Vehicles Symposium.Washington D.C.,USA:IEEE Press,2011:908-913.
[10]Moura T,Valente A,Sousa A,et al.Traffic Sign Recognition for Autonomous Driving Robot[C]//Proceedings of IEEE International Conference on Autonomous Robot System and Competitions.Washington D.C.,USA:IEEE Press,2014:303-308.
[11]Maldonado B S,Acevedo R J,Lafuente A S,et al.An Optimization on Pictogram Identification for the Roadsign Recognition Task Using SVM s[J].Computer Vision and Image Understanding,2010,114(3):373-383.
[12]Itti L,Koch C,Niebur E.Model of Saliency-based Visual Attention for Rapid Scene Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(11):1254-1259.
[13]Achanta R,Hemami S,Estrada F,et al.Frequency-tuned Salient Region Detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.Washington D.C.,USA:IEEE Press,2009:1597-1604.
[14]张志佳,李文强,张丹,等.基于颜色与形状特征的交通标志检测方法[J].计算机技术与发展,2015,25(7):174-178.
[15]Graham R L.An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set[J].Information Processing Letters,1972,1(4):132-133.
[16]周培德,付梦印.寻求简单多边形凸壳的线性时间算法[J].计算机工程与科学,2002,24(3):1-2,44.
[17]王涛,刘文印,孙家广,等.傅里叶描述子识别物体的形状[J].计算机研究与发展,2002,39(12):1714-1719.
[18]Houben S,Stallkamp J,Salmen J,et al.Detection of Traffic Signs in Real-w orld Images:The German Traffic Sign Detection Benchmark[C]//Proceedings of International Joint Conference on Neural Netw orks.Washington D.C.,USA:IEEE Press,2013:1-8.
[19]Belaroussi R,Tarel J P.Angle Vertex and Bisector Geometric Model for Triangular Road Sign Detection[C]//Proceedings of 2009 Workshop on Applications of Computer Vision.Washington D.C.,USA:IEEE Press,2009:577-583.
[20]Brkic'K,Pinz A,egvic'S.Traffic Sign Detection as a Component of an Automated Traffic Infrastructure Inventory System[C]//Proceedings of Annual Workshop of the Austrian Association for Pattern Recognition.Stainz,Austria:[s.n.],2009:1-12.