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浙江大学学报(工学版)
浙江大学学报(工学版)
计算机技术﹑电信技术     
基于非监督特征学习的分叉道路检测算法
杨力1,刘济林2
1.中国计量学院 信息工程学院,浙江 杭州 310018; 2.浙江大学 信息与电子工程学系,浙江 杭州 310027
Road detection algorithm for crossroad based on unsupervised feature learning
YANG Li1, LIU Ji-lin2
1.College of Information Engineering, China Jiliang University, Hangzhou 310018, China; 2.Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
 全文: PDF(3438 KB)  
摘要：

为了解决道路分叉环境中的智能车辆导航问题,提出一种大视场、近距离的道路检测方法.采用安装于车头的鱼眼摄像机,克服了普通相机视野窄、近处存在盲区的问题；通过鱼眼图像重投影,去除鱼眼畸变和透视失真,获得尺度一致的数据块；应用非监督特征学习和逻辑回归分类器,从海量未标记数据中得到原始数据块的稀疏表达,免除了人工标记数据,最后得到路面可通行概率.实验结果表明:此算法在缺乏先验道路几何信息、无手工标记数据的情况下,可以正确地识别分叉道路可通行区域,无视野盲区.
关键词： 特征学习;  逻辑回归;  道路检测;  视觉导航    
Abstract:

A road detection method with large field-of-view and near range was proposed to detect the road area at crossroads for intelligent vehicle navigation. A fisheye camera mounted in the front of the vehicle was used to cover the blind spots in traditional methods. An image reprojection was applied to eliminate radial distortion and perspective projection distortion, thus to get the image blocks of the same scale. Unsupervised features learning was adopted to get the sparse representation of original image blocks, and then road traversable probability was calculated by logistic regression classifier, with no need to label the mass data manually. Experimental results show that this algorithm can recognize the road area at crossroads without blind spots in the absence of prior geometrical information and manual labeling．
Key words: logistic regression    feature learning    road detection    visual navigation
出版日期: 2014-09-30
:  TP 391.4  
基金资助:

国家自然科学基金资助项目（60534070，90820306）
通讯作者: 刘济林，男，教授     E-mail: liujl@zju.edu.cn
作者简介: 杨力（1979-），男，讲师，博士，从事机器视觉研究.E-mail: larryy@zju.edu.cn
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引用本文:

杨力,刘济林. 基于非监督特征学习的分叉道路检测算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2014.09.003.

YANG Li, LIU Ji-lin. Road detection algorithm for crossroad based on unsupervised feature learning. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2014.09.003.

链接本文:

http://www.zjujournals.com/xueshu/eng/CN/10.3785/j.issn.1008-973X.2014.09.003    或    http://www.zjujournals.com/xueshu/eng/CN/Y2014/V48/I9/1558

1］HE Y, WANG H, ZHANG B. Color-based road detection in urban traffic scenes ［J］. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(4):309-318．
［2］RASMUSSEN C. Grouping dominant orientations for ill-structured road following ［C］∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Washington DC: IEEE, 2004: 470-477．
［3］KONG H, AUDIBERT J Y, PONCE J. General road detection from a single image ［J］. IEEE Transactions on Image Processing, 2010, 19（8）: 2211-2220．
［4］URDZIK D, GAMEC J, GAMCOVA. Detection of driving space using Vanishing point estimation ［C］∥Applied Machine Intelligence and Informatics (SAMI), IEEE 8th International Symposium on.Herlany, Slovakia: IEEE, 2010:59-63．
［5］WANG Y, BAI L, FAIRHURST M. Robust road modeling and tracking using condensation ［J］. Intelligent Transportation Systems, IEEE Transactions on, 2008, 9(4):570-579．
［6］TAN C, HONG T, CHANG T, et al. Color model-based real-time learning for road following ［C］∥IEEE Intelligent Transportation Systems Conference. Toronto, Canada: IEEE, 2006: 939-944．
［7］ALVAREZ J M, GEVERS T,DIEGO F， et al. Road geometry classification by adaptive shape models ［J］. IEEE Transactions on Intelligent Transportation Systems, 2013,14: 459-468．
［8］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．
［9］SHINZATO P, WOLF D. A Road following approach using artificial neural networks combinations ［J］. Journal of Intelligent & Robotic Systems, 2011,62(3/4):527-546．
［10］YANG J C, YU K, GONG Y H, et al. Linear spatial pyramid matching using sparse coding for image classification ［C］∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2009: 1794-1801．
［11］HARTLEY R, ZISSERMAN A. Multiple view geometry in computer vision ［M］. Cambridge: Cambridge University Press, 2000．
［12］HAYKIN S O. Neural networks and learning machines ［M］. ［S. l.］: Prentice Hall, 2008: 101-120．
［13］BENGIO Y. Learning deep architectures for AI ［J］. Foundations and Trends in Machine Learning, 2009, 2(1): 11-27．
［14］ALVAREZ J M, LOPEZ A, BALDRICH R. Illuminant-invariant model-based road segmentation ［C］∥IEEE Intelligent Vehicles Symposium. Eindhoven, The Netherlands: IEEE, 2008: 1175-1180.
[1] 谷雨, 李平, 韩波. 基于分层粒子滤波的地标检测与跟踪[J]. J4, 2010, 44(4): 687-691.
[2] 刘加海 白洪欢 黄微凹. 基于彩色和边缘信息融合的道路分割算法[J]. J4, 2006, 40(1): 29-32.