全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于梯度增强和逆透视验证的车道线检测

DOI: 10.3969/j.issn.1006-7043.201401046

Keywords: 先进驾驶辅助系统, 车道线检测, 梯度增强, 逆透视变换, 弱线检测, 虚线检测

Full-Text   Cite this paper   Add to My Lib

Abstract:

为解决高速公路和城市道路上复杂条件下的弱线漏检问题,提出了一种基于梯度增强和逆透视验证的车道线检测方法。该方法使用车道线的结构和对比度特征提取车道线区域,利用提取的车道线区域进行车道线和道路样本的选择,并采用基于模糊线性鉴别分析获得从彩色RGB图像到灰度图像变换的最佳投影系数,以确保车道线和道路像素间的灰度差异最大,从而有效突出道路上的弱线;利用逆透视变换对候选车道线间的空间位置关系进一步验证,以此找回漏检的虚线。不同场景、不同天气状况下的实际道路图像的实验表明,方法具有很好的鲁棒性和准确性。

References

[1]  CHIU K Y, LIN S F. Lane detection using color based segmentation[C]//IEEE Intelligent Vehicle Symposium. Hawaii, USA, 2005: 706-711.
[2]  ROTARU C G, ZHANG T J. Extracting road feature from color image using a cognitive approach[C]//IEEE Intelligent Vehicle Symposium. Parma, Italy, 2004: 298-303.
[3]  MARGRIT B, ESIN H, LARRY S D. Real-time multiple vehicle detection and tracking from a moving vehicle[J]. Machine Vision and Applications, 2000, 12(2): 69-83.
[4]  HUNJAE Y, UKIL Y, KWANGHOON S. Gradient-enhancing conversion for illumination-robust lane detection[J]. IEEE Transactions on Intelligence Transportation Systems, 2013, 14(3): 1083-1094.
[5]  SOUTHHALL J B, TAYLOR C. Stochastic road shape estimation[C]//Computer Vision. Vancouver, Canada, 2001: 205-212.
[6]  YU B, JAIN A K. Lane boundary detection using a multi-resolution Hough transform[C]//Image Processing, Washington DC, USA, 1997: 748-751.
[7]  BORKAR A, HAYES M, STMTH M T. Detecting Lane markers in complex urban environments[C]//IEEE 7th International Conference on Mobile Adhoc and Sensor Systems. San Francisco, USA, 2010.
[8]  YOUNG U K Y, OH S Y. Three-feature based automatic lane detection algorithm for autonomous driving[J]. IEEE Transactions on Intelligent Transportation System, 2003, 4(4): 219-224.
[9]  WANG Y, TEOH E K, SHEN D. Lane detection and tracking using B-Snake[J]. Image and Vision Computing, 2004, 22(4): 269-280.
[10]  YAGI Y, BRADY M, KAWASAKI Y, et al. Active contour road model for smart vehicle[C]//15th International Conference on Pattern Recognition. Barcelona, Spain, 2000: 819-822.
[11]  SOUTHBALL B, TAYLOR C J. Stochastic road shape estimation[C]//ICCV Vancouver, Canada, 2001: 205-212.
[12]  EIDEHALL A, GUSTAFSSON F. Obtaining reference road geometry parameters from recorded sensor data[C]//IEEE Intelligent Vehicle Symposium. Tokyo, Japan, 2006: 256-260.
[13]  MCCALL J C, TRIVEDI M M. Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation[J]. IEEE Trans Intell Transp Syst, 2006, 7(1): 20-37.
[14]  GUIDUCCI A. Camera calibration for road application[J]. Comput. Vis.Image Underst, 2000, 79: 250-266.
[15]  CHEN Q, WANG H. A real-time lane detection algorithm based on a hyperbola-pair model[C]//Intelligent Vehicle Symposium. Tokyo, Japan, 2006: 510-515.
[16]  RUYI J, REINHARD K, TOBI V, et al. Lane detection and tracking using a new lane model and distance transform[J]. Machine Vision and Applications, 2011, 22(4): 721-737.
[17]  DANESCU R, NEDEVSCHI S. New results in stereovision based lane tracking[C]//Intelligent Vehicles Symposium. Baden, Germany, 2011: 230-235.
[18]  KAPLAN K, KURTUL C, AKIN H L. Fast lane tracking for autonomous urban driving using hidden Markov models and multiresolution Hough transform[J]. Industrial Robot, 2010, 37(3): 273-278.
[19]  CHENG H Y, JENG B S, TSENG P T, et al. Lane detection with moving vehicles in the traffics scenes[J]. IEEE trans on Intelligent Vehicle Symposium, 2006, 7(4): 571-582.
[20]  WANG J, WU Y, LIANG Z, et al. Lane detection and tracking using a layered approach[C]//IEEE Int Conf Inf. Autom. Harbin, China, 2010: 1735-1740.
[21]  KWAK K C, PEDRYCZ W. Face recognition using a fuzzy fisherface classifier[J]. Pattern Recognition, 2005(38): 1717-1732.
[22]  SUN T Y, TSAI S J, CHAN V. HSI color model based lane-marking detection[C]//Proc. IEEE Intelligent Transportation System Conf. Toronto, Canada, 2006: 1168-1172.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133