基于边缘的车道线识别算法的研究与实现
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摘要
随着社会经济的发展,交通运输问题越来越突出,迫切要求采用现代化的管理方法来实现交通管理,这样就引发了对智能交通系统(ITS)的研究。车辆辅助驾驶是智能交通系统的重要组成部分,在提高汽车的主动安全性能和减少交通事故方面有着广阔的应用前景。在过去十几年里,基于视觉的道路感知技术被越来越多地应用于辅助驾驶系统中,因此研究道路的感知技术对车载辅助驾驶系统具有重要的意义。
本文研究的内容是基于车载单目视觉的道路识别技术,本文的基本思想是：车辆运行起始阶段,运行车道线检测模块,实现车道线的识别,车辆进入路径稳定时期,则启动车道线跟踪模块,得到车道线信息；当车辆转向频繁或幅度较大时,或发现道路状况发生变化时,转入车道线检测模块。研究内容分为两大部分,第一部分研究鱼眼图像的图像矫正问题和道路平面上消失点的计算方法,为车道线识别提供技术支持。第二部分主要研究车道线检测和跟踪算法。本文在对现有的车道线检测算法分析比较的基础上,基于Sobel算子和Hough变换原理提出道路边缘检测算法和道路直线检测算法,进而设计和实现车道线检测算法；本文总结现有运动目标跟踪的算法,设计和实现车道线跟踪算法。
实验结果表明本文所提出的车道线识别算法,能很好地识别出道路车道区域,具有一定的鲁棒性和实时性,为辅助驾驶系统融合多种道路识别算法提供了重要的依据。
With the development of economy, modern management methods are needed to administer traffic on account of problems of traffic transportation being more and more severe, which begets the research on Intelligent Transportation System-ITS. Driver Assistance System is an important component of Intelligent Transportation System--ITS. Driver Assistance System has bright prospects, especially in automobile safety improvement and accident avoidance measurement. Over the past decades of years, vision-based road apperceive algorithms have been used in Driver Assistance System. So the study of vision-based road apperception algorithms is very important to Driver Assistance System.
The research topic of the thesis is on-board monocular vision-based road recognition. The basic thinking of the thesis is as follows. Firstly, the lane recognition calls the lane detection module. When in the steady-going stage, the lane recognition calls the lane tracking module. If the vehicle turns frequently, or the angle of the turning is big, or the road condition has changed, the lane recognition calls the detection module again. The thesis consists of two parts. In the first part, the emendation of the fisheye image and the compute of the vanishing point are studied, which provide the base for the lane recognition. In the second part, the lane detection and the lane tracking are studied mainly. In the lane detection section, after analyzing and comparing the current road detection algorithms, the edge detection algorithm and the straight line detection algorithm are studied based on the principle of the Sobel and Hough. In lane tracking part, after summarizing the current road tracking algorithms, a tracking algorithm is designed and implemented.
Experiments show that the method can get a reliable result, and it is robust and real-time. The thesis is useful for the fusion of other road detection algorithms in Driver Assistance System.
本文研究的内容是基于车载单目视觉的道路识别技术,本文的基本思想是：车辆运行起始阶段,运行车道线检测模块,实现车道线的识别,车辆进入路径稳定时期,则启动车道线跟踪模块,得到车道线信息；当车辆转向频繁或幅度较大时,或发现道路状况发生变化时,转入车道线检测模块。研究内容分为两大部分,第一部分研究鱼眼图像的图像矫正问题和道路平面上消失点的计算方法,为车道线识别提供技术支持。第二部分主要研究车道线检测和跟踪算法。本文在对现有的车道线检测算法分析比较的基础上,基于Sobel算子和Hough变换原理提出道路边缘检测算法和道路直线检测算法,进而设计和实现车道线检测算法；本文总结现有运动目标跟踪的算法,设计和实现车道线跟踪算法。
实验结果表明本文所提出的车道线识别算法,能很好地识别出道路车道区域,具有一定的鲁棒性和实时性,为辅助驾驶系统融合多种道路识别算法提供了重要的依据。
With the development of economy, modern management methods are needed to administer traffic on account of problems of traffic transportation being more and more severe, which begets the research on Intelligent Transportation System-ITS. Driver Assistance System is an important component of Intelligent Transportation System--ITS. Driver Assistance System has bright prospects, especially in automobile safety improvement and accident avoidance measurement. Over the past decades of years, vision-based road apperceive algorithms have been used in Driver Assistance System. So the study of vision-based road apperception algorithms is very important to Driver Assistance System.
The research topic of the thesis is on-board monocular vision-based road recognition. The basic thinking of the thesis is as follows. Firstly, the lane recognition calls the lane detection module. When in the steady-going stage, the lane recognition calls the lane tracking module. If the vehicle turns frequently, or the angle of the turning is big, or the road condition has changed, the lane recognition calls the detection module again. The thesis consists of two parts. In the first part, the emendation of the fisheye image and the compute of the vanishing point are studied, which provide the base for the lane recognition. In the second part, the lane detection and the lane tracking are studied mainly. In the lane detection section, after analyzing and comparing the current road detection algorithms, the edge detection algorithm and the straight line detection algorithm are studied based on the principle of the Sobel and Hough. In lane tracking part, after summarizing the current road tracking algorithms, a tracking algorithm is designed and implemented.
Experiments show that the method can get a reliable result, and it is robust and real-time. The thesis is useful for the fusion of other road detection algorithms in Driver Assistance System.
引文
1.王征平,邵迈,蔡少渠.智能运输系统的产生与发展[J],智能交通,2003：48-50.
2.孙晋文.智能运输系统与汽车的智能化技术[J],汽车研究与开发,2000：15-16.
3. H. Tokuyama. Asia-pacific projects status and plans U.S. Dept. of Transportation [J].
4.余天洪,王荣本,顾柏园,等.基于机器视觉的智能车辆前方道路边界及车道标识识别方法综述[J].公路交通科技,2006,23(1).
5. M Bertozzi, A VBroggi. GOLD:A Parallel Real Time Stereo Vision System for Generic Obstacle and Lane Detection [J].IEEE Trans.on Image Proc,1998,7 (1):62-811.
6.陆建业,杨明,等.Vision-based Real-time Road Detection IN Rrban Traffic[C]. Proc. SPIE Real-Time Imaging VI, Nasser Kehtamavaz,2002,4666:75-82.
7. R Turchetto, R Manduchi.Visual Curb Localization for Autonomous. Navigation [C].IEEE/RSJ IROS'03, Las Vegas, October 2003.
8. Axel Gern, Rainer Moebus, Uwe Franke.Vision-based Lane Recognition under Adverse Weather Conditions Using Optical Flow [C].Proc.of IEEE Intelligent Vehicle Symposium, 2002.
9.程洪,郑南宁,等.基于主元神经网络和K均值的道路识别算法[J].西安交通大学学报,2003,37(8)：812-815.
10.徐友春.智能车辆视觉的与GPS综合导航方法的研究[D].吉林大学博士学位论文,2001.
11.周欣,黄席樾,黎昱.基于单目视觉的高速公路车道保持与距离测量[J].中国图像图形学报.2003,8(A5)：590-595.
12.Roland Chapuis, Jean Laneurit.Accurate Vision Based Road Tracker[C].Proc. of IEEE Intelligent Vehicle Symposium,2002.
13.Princen J, Illingworth J, Kittler J. A formal definition of the Hough Transform properties and relationships. J Mathematical Imaging and Vision,1999,1:153-148.
14.Bergen J R, Shvayster H. A probabilistic algorithm for computing Hough Transform. J Algorithm,1998,12:639-656.
15.Hu Z Y, Ma S D. Three conditions of a good line parameterization. Pattern Recognition Letters,1999,16:385-388.
16.Hough P V C. Method and means of recognizing complex patterns. U. S. Patent 3069654, 1998.
17.Cypher R E, Sanz J L C, Snyder L. The Hough Transform has Complexity on mesh connected computer. SIAM J Computing,2000,19:805-820.
18.马颂德,张正友.计算机视觉—计算理论与算法基础[M],北京：科学出版社,1998,52-71.
19.王丹.车辆辅助驾驶-运动中二维到三维映射算法的研究与实现[D],沈阳：东北大学,2006.
20.王大宇,崔汉国,陈军.鱼眼图像轮廓提取及校正研究[J],计算机工程与设计,2007,28(12).
21.Jacquin A E. Fractal image coding:a review[J]. Proceedings of IEEE,1999,81(10):1451-1465.
22. Bellen A, Guglielmi N and Zennaro M. Numerical stability of nonlinear delay differential equations of neutral type[J]. J.Comput.Appl.Math.,2000,125:251-263.
23.Burrage K and Butcher J C. Stability criteria,for implicit Runge-Kutta methods[J]. SLAM.J.Numer.Anal.,1998,16:46-57.
24.Hale J K and Lunel S M.V. Introduction to functional differential equations[M].Springer-Verlag,Berlin,1998.
25.Hu G D and Mitsui T. Stability analysis of numerical methods for systems of neutral delay-differential equations[J].BIT,1999,35:504-515.
26. Huang C M, Fu H Y, Li S F and Chen G N. Stability analysis of Runge-Kutta methods for nonlinear delay differential equations[J].BIT,1999,39:270-280.
27.Huang C M, Li S F, Fu H Y and Chen G N. Stability and error analysis of one-leg methods for nonlinear delay differential equations[J]. J.Comput.Appl.Math.,1999.103:263-279.
28.黄乘明,李寿佛.θ-方法的非线性渐近稳定性[J].高等学校计算数学学报,2000,22：335-340.
29.Huang C M and Chang Q S. Linear stability of general linear methods for systems of neutral delay differential equations[J]. Appl. Math. Letters,2001,14:1017-1021.
30.黄有度,苏化明.一种鱼眼图像到透视投影图像的变换模型[J].系统仿真学报,2005,17(1).
31.孙涵,任明武,唐振民,杨静宇.基于机器视觉的智能车辆导航综述[J].公路交通科技.2005,5,132-135.
32.杨明,陆建业,王宏,张钹.基于视觉的道路跟踪[J].模式识别与人工智能,2001,14(2)：186—193.
33.魏存伟.道路感知技术研究与实现[D],沈阳：东北大学,2008.
34.J D Crisman, C E Thorpe. SCARF:A Color Vision System That Tracks Roads and Intersections[J]. IEEE Transactions on Robotics and Automation,1999,9(1):49-58.
35.左森,郭晓松,万敬,郭君斌.基于支持向量机的实时路面检测算法[J].计算机工程.2007,4,225-227.
36.Foedisch M,Takeuchi. Adaptive Real-Time Road Detection Using Neural Networks Intelligent Transportation Systems[J]. The 7th International IEEE Conference on 3-6 Oct. 2004 Page(s):167-172.
37.Klaus Hansen, Jens Damgaard Andersen. Understanding the Hough Transform:Hough Cell Support and Its Utilisation [J]. Image and Vision Computing,2000,15 (3):205-218.
38.刘长鹏.基于数据并行的道路目标物分割算法研究与实现[D],沈阳：东北大学,2008.
39.乔洁,李京华,杨志荣.基于Hough变换的道路边缘提取[J].交通与计算机,2008,26(140).
40. H Freeman. Boundary Encoding and Processing[M]. Picture Processing and Psychop ictorics, Academic, New York,1999.241-266.
41.史册,徐胜荣,荆仁杰等.实时图像处理中一种快速的直线检测算法[J].浙江大学学报(工学版),1999,33(5)：482-486.
42.孙涵,任明武,杨静宇.一种快速实用的直线检测算法[J].计算机应用研究.2006,2,256-260.
43.Klaus Hansen, Jens Damgaard Andersen. Understanding the Hough Transform:Hough Cell Support and Its Utilisation [J]. Image and Vision Computing,1998,15 (3):205-218.
44.John Immerk r. Some Remarks on the StraightLine Hough Transform [J]. Pattern Recognition Letters,1998,19 (12):1133-1135.
45.徐勇,甘新胜.运动目标视频检测与跟踪方法[J].指挥控制与仿真,2008,30(2).
46.Benjamin Cofman, David Beymer, Philip McLauchlan, etc. A real-time computer vision system for vehicle tracking and traffic surveillance[J]. In Transportation Research Part C, 1998(6):271-288.
47. Robert T. Collins etc. A system for video surveillance and Monitoring[R]. A final report.CMU-RI-TR-00-12.
48.李天庆,张毅,刘志,等.Snake模型综述[J].计算机工程,2005,31(9)：1-3.
49.Xuefeng song, Ram Nevatia. A model-based vehicle segmentation method for tracking[A]. In:Tenth IEEE International Conference,2005(2):1124-1131.
5O.Guerra C, Hambrush S. Parallel algorithms for line detection on a mesh. J Parallel Distributed Computing,1999,6:1-19.
51.Pan Y, Chuang Y H. Parallel Hough Transforms on SIMD hypercube arrays. In:Proc 2000 International Conference on Parallel Processing, University Park, PA,1990.83-86.
52.Jenq J F, Sahni S. Reconfigurable algorithms for the Hough Transform. In:Proc 2000 International Conference on Parallel Processing, Boca Raton, FL,2001.34-41.
53.孙丰荣,刘积仁.快速霍夫变换算法[J].计算机学报,2001,24(10).
54.阴国富.基于阈值法的图像分割技术.http://www.eccn.com/xsj07/xsj081933w.asp.
2.孙晋文.智能运输系统与汽车的智能化技术[J],汽车研究与开发,2000：15-16.
3. H. Tokuyama. Asia-pacific projects status and plans U.S. Dept. of Transportation [J].
4.余天洪,王荣本,顾柏园,等.基于机器视觉的智能车辆前方道路边界及车道标识识别方法综述[J].公路交通科技,2006,23(1).
5. M Bertozzi, A VBroggi. GOLD:A Parallel Real Time Stereo Vision System for Generic Obstacle and Lane Detection [J].IEEE Trans.on Image Proc,1998,7 (1):62-811.
6.陆建业,杨明,等.Vision-based Real-time Road Detection IN Rrban Traffic[C]. Proc. SPIE Real-Time Imaging VI, Nasser Kehtamavaz,2002,4666:75-82.
7. R Turchetto, R Manduchi.Visual Curb Localization for Autonomous. Navigation [C].IEEE/RSJ IROS'03, Las Vegas, October 2003.
8. Axel Gern, Rainer Moebus, Uwe Franke.Vision-based Lane Recognition under Adverse Weather Conditions Using Optical Flow [C].Proc.of IEEE Intelligent Vehicle Symposium, 2002.
9.程洪,郑南宁,等.基于主元神经网络和K均值的道路识别算法[J].西安交通大学学报,2003,37(8)：812-815.
10.徐友春.智能车辆视觉的与GPS综合导航方法的研究[D].吉林大学博士学位论文,2001.
11.周欣,黄席樾,黎昱.基于单目视觉的高速公路车道保持与距离测量[J].中国图像图形学报.2003,8(A5)：590-595.
12.Roland Chapuis, Jean Laneurit.Accurate Vision Based Road Tracker[C].Proc. of IEEE Intelligent Vehicle Symposium,2002.
13.Princen J, Illingworth J, Kittler J. A formal definition of the Hough Transform properties and relationships. J Mathematical Imaging and Vision,1999,1:153-148.
14.Bergen J R, Shvayster H. A probabilistic algorithm for computing Hough Transform. J Algorithm,1998,12:639-656.
15.Hu Z Y, Ma S D. Three conditions of a good line parameterization. Pattern Recognition Letters,1999,16:385-388.
16.Hough P V C. Method and means of recognizing complex patterns. U. S. Patent 3069654, 1998.
17.Cypher R E, Sanz J L C, Snyder L. The Hough Transform has Complexity on mesh connected computer. SIAM J Computing,2000,19:805-820.
18.马颂德,张正友.计算机视觉—计算理论与算法基础[M],北京：科学出版社,1998,52-71.
19.王丹.车辆辅助驾驶-运动中二维到三维映射算法的研究与实现[D],沈阳：东北大学,2006.
20.王大宇,崔汉国,陈军.鱼眼图像轮廓提取及校正研究[J],计算机工程与设计,2007,28(12).
21.Jacquin A E. Fractal image coding:a review[J]. Proceedings of IEEE,1999,81(10):1451-1465.
22. Bellen A, Guglielmi N and Zennaro M. Numerical stability of nonlinear delay differential equations of neutral type[J]. J.Comput.Appl.Math.,2000,125:251-263.
23.Burrage K and Butcher J C. Stability criteria,for implicit Runge-Kutta methods[J]. SLAM.J.Numer.Anal.,1998,16:46-57.
24.Hale J K and Lunel S M.V. Introduction to functional differential equations[M].Springer-Verlag,Berlin,1998.
25.Hu G D and Mitsui T. Stability analysis of numerical methods for systems of neutral delay-differential equations[J].BIT,1999,35:504-515.
26. Huang C M, Fu H Y, Li S F and Chen G N. Stability analysis of Runge-Kutta methods for nonlinear delay differential equations[J].BIT,1999,39:270-280.
27.Huang C M, Li S F, Fu H Y and Chen G N. Stability and error analysis of one-leg methods for nonlinear delay differential equations[J]. J.Comput.Appl.Math.,1999.103:263-279.
28.黄乘明,李寿佛.θ-方法的非线性渐近稳定性[J].高等学校计算数学学报,2000,22：335-340.
29.Huang C M and Chang Q S. Linear stability of general linear methods for systems of neutral delay differential equations[J]. Appl. Math. Letters,2001,14:1017-1021.
30.黄有度,苏化明.一种鱼眼图像到透视投影图像的变换模型[J].系统仿真学报,2005,17(1).
31.孙涵,任明武,唐振民,杨静宇.基于机器视觉的智能车辆导航综述[J].公路交通科技.2005,5,132-135.
32.杨明,陆建业,王宏,张钹.基于视觉的道路跟踪[J].模式识别与人工智能,2001,14(2)：186—193.
33.魏存伟.道路感知技术研究与实现[D],沈阳：东北大学,2008.
34.J D Crisman, C E Thorpe. SCARF:A Color Vision System That Tracks Roads and Intersections[J]. IEEE Transactions on Robotics and Automation,1999,9(1):49-58.
35.左森,郭晓松,万敬,郭君斌.基于支持向量机的实时路面检测算法[J].计算机工程.2007,4,225-227.
36.Foedisch M,Takeuchi. Adaptive Real-Time Road Detection Using Neural Networks Intelligent Transportation Systems[J]. The 7th International IEEE Conference on 3-6 Oct. 2004 Page(s):167-172.
37.Klaus Hansen, Jens Damgaard Andersen. Understanding the Hough Transform:Hough Cell Support and Its Utilisation [J]. Image and Vision Computing,2000,15 (3):205-218.
38.刘长鹏.基于数据并行的道路目标物分割算法研究与实现[D],沈阳：东北大学,2008.
39.乔洁,李京华,杨志荣.基于Hough变换的道路边缘提取[J].交通与计算机,2008,26(140).
40. H Freeman. Boundary Encoding and Processing[M]. Picture Processing and Psychop ictorics, Academic, New York,1999.241-266.
41.史册,徐胜荣,荆仁杰等.实时图像处理中一种快速的直线检测算法[J].浙江大学学报(工学版),1999,33(5)：482-486.
42.孙涵,任明武,杨静宇.一种快速实用的直线检测算法[J].计算机应用研究.2006,2,256-260.
43.Klaus Hansen, Jens Damgaard Andersen. Understanding the Hough Transform:Hough Cell Support and Its Utilisation [J]. Image and Vision Computing,1998,15 (3):205-218.
44.John Immerk r. Some Remarks on the StraightLine Hough Transform [J]. Pattern Recognition Letters,1998,19 (12):1133-1135.
45.徐勇,甘新胜.运动目标视频检测与跟踪方法[J].指挥控制与仿真,2008,30(2).
46.Benjamin Cofman, David Beymer, Philip McLauchlan, etc. A real-time computer vision system for vehicle tracking and traffic surveillance[J]. In Transportation Research Part C, 1998(6):271-288.
47. Robert T. Collins etc. A system for video surveillance and Monitoring[R]. A final report.CMU-RI-TR-00-12.
48.李天庆,张毅,刘志,等.Snake模型综述[J].计算机工程,2005,31(9)：1-3.
49.Xuefeng song, Ram Nevatia. A model-based vehicle segmentation method for tracking[A]. In:Tenth IEEE International Conference,2005(2):1124-1131.
5O.Guerra C, Hambrush S. Parallel algorithms for line detection on a mesh. J Parallel Distributed Computing,1999,6:1-19.
51.Pan Y, Chuang Y H. Parallel Hough Transforms on SIMD hypercube arrays. In:Proc 2000 International Conference on Parallel Processing, University Park, PA,1990.83-86.
52.Jenq J F, Sahni S. Reconfigurable algorithms for the Hough Transform. In:Proc 2000 International Conference on Parallel Processing, Boca Raton, FL,2001.34-41.
53.孙丰荣,刘积仁.快速霍夫变换算法[J].计算机学报,2001,24(10).
54.阴国富.基于阈值法的图像分割技术.http://www.eccn.com/xsj07/xsj081933w.asp.