基于方向边缘匹配的人行横道与停止线检测

doi:10.3969/j.issn.1000-3428.2013.06.058

计算机工程 ›› 2013, Vol. 39 ›› Issue (6): 261-265,282.doi: 10.3969/j.issn.1000-3428.2013.06.058

基于方向边缘匹配的人行横道与停止线检测

谢锦^1,2，蔡自兴¹，李昭¹，尤作¹

(1. 中南大学信息科学与工程学院，长沙 410083；2. 湖南师范大学工学院，长沙 410081)

收稿日期:2012-06-25 出版日期:2013-06-15 发布日期:2013-06-14
作者简介:谢锦(1974－)，女，博士研究生、CCF会员，主研方向：模式识别，图像分析；蔡自兴，教授、博士生导师；李昭、尤作，硕士研究生
基金项目:
国家自然科学基金资助重大项目(90820302)；国家自然科学基金资助项目(60805027)；博士点基金资助项目(2008053300 05)

Pedestrian Crossing and Stop Line Detection Based on Oriented-edge Match

XIE Jin ^1,2, CAI Zi-xing ¹, LI Zhao ¹, YOU Zuo ¹

(1. School of Information Science and Engineering, Central South University, Changsha 410083, China; 2. College of Polytechnic, Hunan Normal University, Changsha 410081, China)

Received:2012-06-25 Online:2013-06-15 Published:2013-06-14

摘要/Abstract

摘要： 针对智能车辆视觉导航系统的路面标线实时感知问题，提出基于方向边缘匹配实现人行横道和停止线的检测算法。利用逆透视映射表快速建立车辆前方感兴趣区域俯视图，提取图像垂直边缘和水平边缘，通过亮度上升与下降相邻边缘匹配检测定位人行横道与停止线。实验结果表明，该检测算法满足实时性要求，具有强鲁棒性，能应用于复杂城市道路环境。

关键词: 方向边缘, 逆透视映射, 感兴趣区域, 线性拟合, 匹配, 随机抽样一致性

Abstract: In order to solve the real-time road marking recognition problem in the intelligent vehicle vision navigation system, a novel pedestrian crossing and stop line detection algorithm based on oriented-edge match is proposed. It builds top-view image through Inverse Perspective Mapping(IPM) transformation by a mapping table to accelerate IPM process. It abstracts vertical oriented-edge and horizontal oriented-edge. The pedestrian crossing and stop line can be detected by matching adjacent up and down edges to bands. Experimental results illustrate that the algorithm is effective for real-time analysis and robust under difficult urban road circumstances.

Key words: oriented-edge, Inverse Perspective Mapping(IPM), Region of Interest(ROI), linear fitting, match, random sample consensus

中图分类号:

TP391.41

谢锦, 蔡自兴, 李昭, 尤作. 基于方向边缘匹配的人行横道与停止线检测[J]. 计算机工程, 2013, 39(6): 261-265,282.

XIE Jin, CA Zi-Xin, LI Zhao, YOU Zuo. Pedestrian Crossing and Stop Line Detection Based on Oriented-edge Match[J]. Computer Engineering, 2013, 39(6): 261-265,282.

参考文献

[1] Uddin M S, Shioyama T. Detection of Pedestrian Crossing Using Biploarity Feature——An Image-based Technique[J]. IEEE Trans. on Intelligent Transportation Systems, 2005, 6(4): 439-445.
[2] Uddin M S, Shioyama T. Bipolarity and Projective Invariant- based Zebra-crossing Detection for the Visually Im- paired[C]//Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. [S. l.]: IEEE Computer Society, 2005.
[3] Sichelschmidt S, Haselhoff A, Kummert A. Pedestrian Crossing Detecting as a Part of Urban Pedestrian Safety System[C]//Proc. of IEEE Intelligent Vehicles Symposium. San Diego, USA: IEEE Press, 2010: 840-844.
[4] Choi J, Lee J, Kim D, et al. Environment-detection-and- mapping Algorithm for Autonomous Driving in Rural or Off-road Environment[J]. IEEE Trans. on Intelligent Transportation Systems, 2012, 13(2): 974-982.
[5] 梁昆, 杨明, 王春香, 等. 基于低精度GPS的智能车视觉导航方法[J]. 上海交通大学学报, 2011, 45(2): 168-172.
[6] Shamir L, Delaney J, Orlov N, et al. Image Segmentation and Pattern Recognition for Road Marking Analysis[C]]//Proc. of IEEE International Symposium on Industrial Electronics. [S. l.]: IEEE Press, 2004: 727-732.
(下转第282页)
(上接第265页)

[7] Bertozzi M, Broggi A, Fascioli A. Stereo Inverse Per- spective Mapping: Theory and Application[J]. Image and Vision Computing, 1998, 16(8): 585-590.
[8] Lim Y, Lee M, Lee C, et al. Improvement of Stereo Vision- based Position and Velocity Estimation and Tracking Using a Stripe-based Disparity Estimation and Inverse Perspective Map-based Extended Kalman Filter[J]. Optics and Lasers in Engineering, 2010, 48(9): 859-868.
[9] Canny J. A Computational Approach for Edge Detection[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[10] Koenederink J J. The Structure of Images[J]. Biological Cybernetics, 1984, 50(5): 363-370.
[11] Fischler M A, Bolles R C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
编辑顾逸斐

[1]	徐龙壮, 彭力, 朱凤增. 多任务金字塔重叠匹配的行人重识别方法[J]. 计算机工程, 2021, 47(1): 239-245,254.
[2]	龙恳, 李伟, 鲁江丽, 蒋明均, 隆泉. 基于匹配理论的NOMA异构网络资源分配算法[J]. 计算机工程, 2021, 47(1): 165-171.
[3]	陈泽, 叶学义, 钱丁炜, 魏阳洋. 基于改进Faster R-CNN的小尺度行人检测[J]. 计算机工程, 2020, 46(9): 226-232,241.
[4]	黄坤, 钱军浩, 王江文. 基于改进FREAK的图像匹配算法[J]. 计算机工程, 2020, 46(8): 250-257.
[5]	韩舒艳, 努尔买买提·黑力力. 选择性隐藏树型访问结构的CP-ABE方案[J]. 计算机工程, 2020, 46(7): 150-158.
[6]	刘治国, 宋广跃, 蔡文珠, 刘庆利. 基于TextRank算法的未知网络协议帧定位方法[J]. 计算机工程, 2020, 46(7): 179-184.
[7]	丁青锋, 高鑫鹏, 邓玉前. 毫米波中继网络离散正交匹配追踪混合预编码算法[J]. 计算机工程, 2020, 46(7): 192-197,205.
[8]	程希希, 张艳玲, 田军委. 基于模板匹配的快速角点检测方法[J]. 计算机工程, 2020, 46(7): 222-227.
[9]	王金鹤, 苏翠丽, 孟凡云, 车志龙, 谭浩, 张楠. 基于非对称空间金字塔池化的立体匹配网络[J]. 计算机工程, 2020, 46(7): 228-234,242.
[10]	张一飞, 李新福, 田学东. 融合边缘特征的SAD立体匹配算法[J]. 计算机工程, 2020, 46(4): 236-240,246.
[11]	彭大芹, 李靖. 面向NB-IoT终端的指纹匹配定位改进算法[J]. 计算机工程, 2020, 46(3): 178-183,191.
[12]	庞智华, 齐臣坤. 一种鲁棒的单目视觉里程计算法[J]. 计算机工程, 2020, 46(12): 80-87.
[13]	李向军, 周勇, 刘韬, 刘伯成, 罗铭. 基于最小点对成本的二维轮廓精确匹配与分析方法[J]. 计算机工程, 2020, 46(12): 231-237.
[14]	甄卓, 陈玉泉. 基于依存关系与神经网络的文本匹配模型[J]. 计算机工程, 2019, 45(8): 210-216,223.
[15]	杨罡, 晋涛, 王大伟, 曹京津, 张娜, 严碧武, 李涛, 程远. 改进的代价聚集快速立体匹配算法[J]. 计算机工程, 2019, 45(7): 268-272,281.

基于方向边缘匹配的人行横道与停止线检测

Pedestrian Crossing and Stop Line Detection Based on Oriented-edge Match

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10