基于立体视觉的汽车安全车距预警系统研究
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摘要
汽车安全车距预警系统作为智能交通系统的一个重要分支是近年来的研究热点。随着立体视觉算法的发展和计算机硬件处理平台速度的提高,立体视觉技术在智能交通系统中的应用越来越广泛。
本文在对立体视觉算法进行深入研究和分析后,考虑试验成本和未来的市场竞争力,设计制作了双目横向平行模式立体视觉系统试验平台,在此平台上实现了一种高速公路上的前方车辆检测识别和安全车距测量方案。首先对摄像机成像模型以及摄像机标定的方法进行探讨,确定采用改进的张氏平面标定方法,通过试验得出立体视觉系统的内外参数;然后根据高速公路上前方车辆的特征,研究了基于车辆底部下缘阴影的前方车辆检测方法,进行了相关试验,得到目标车辆在左右两幅图像中的位置;最后提出一种应用于高速公路上的安全距离测量方法,该方法利用异面直线公垂线中点法计算出目标车辆到摄像头的距离。试验结果表明:该系统测量范围广,适用于远距离测量,测量精度能够满足智能交通中车辆安全距离测量的实际要求。在算法研究的基础上,自行设计开发出基于德州仪器公司TMS320C6000系列的
TMS320DM642芯片的实时处理硬件系统,实现双通道图像采集、实时视频图像处理、视频输出等功能。该系统应用于汽车安全车距预警系统,目前可以实现单帧图像的处理和显示,处理时间为0.1S。
As one of the research topic of Intelligent Transportation System(ITS),early-warning-of-collision system has been drawn much attention in recent years. With the development of stereo vision algorithms and the improved speed of hardware, stereo vision is widely used in ITS systems.
For decreasing experiment cost and improving future market competition power, this thesis designs and develops a horizontal stereo vision experiment platform through deeply research and analysis of stereo vision algorithms. And then, we propose a scheme including safe headway measurement, preceding vehicle detection and recognition for express way. Firstly, by study and analysis of camera imaging models and camera calibration methods, this thesis proposed a new calibration method based on the traditional calibration method, which obtained internal and external parameters by calibration experiments. Secondly, by the shade character of the front car, the different coordinates of the target car in the binocular images can be detected. Finally, a Secure-Vehicles- Spacing-Measurement(SVSM) program applied to expressway traffic was proposed, the distance between the target vehicle to the camera can be calculated by employing the common perpendicular midpoint method. The results show that the measurement program can measure a wide range of distance and can meet the needs of a SVSM for intelligent vehicles, which is an effective way for measuring the distance to the front car.
Following the research of the algorithms, a real-time image processing system based on TMS320DM642 was designed in this thesis. The system has the function of real-time dual channel video acquisition, real-time image processing, video output and so on. The system was applied in previous collision warning system and can carry out image processing in the frame-by-frame way, the processing time of one frame was 0.1s.
本文在对立体视觉算法进行深入研究和分析后,考虑试验成本和未来的市场竞争力,设计制作了双目横向平行模式立体视觉系统试验平台,在此平台上实现了一种高速公路上的前方车辆检测识别和安全车距测量方案。首先对摄像机成像模型以及摄像机标定的方法进行探讨,确定采用改进的张氏平面标定方法,通过试验得出立体视觉系统的内外参数;然后根据高速公路上前方车辆的特征,研究了基于车辆底部下缘阴影的前方车辆检测方法,进行了相关试验,得到目标车辆在左右两幅图像中的位置;最后提出一种应用于高速公路上的安全距离测量方法,该方法利用异面直线公垂线中点法计算出目标车辆到摄像头的距离。试验结果表明:该系统测量范围广,适用于远距离测量,测量精度能够满足智能交通中车辆安全距离测量的实际要求。在算法研究的基础上,自行设计开发出基于德州仪器公司TMS320C6000系列的
TMS320DM642芯片的实时处理硬件系统,实现双通道图像采集、实时视频图像处理、视频输出等功能。该系统应用于汽车安全车距预警系统,目前可以实现单帧图像的处理和显示,处理时间为0.1S。
As one of the research topic of Intelligent Transportation System(ITS),early-warning-of-collision system has been drawn much attention in recent years. With the development of stereo vision algorithms and the improved speed of hardware, stereo vision is widely used in ITS systems.
For decreasing experiment cost and improving future market competition power, this thesis designs and develops a horizontal stereo vision experiment platform through deeply research and analysis of stereo vision algorithms. And then, we propose a scheme including safe headway measurement, preceding vehicle detection and recognition for express way. Firstly, by study and analysis of camera imaging models and camera calibration methods, this thesis proposed a new calibration method based on the traditional calibration method, which obtained internal and external parameters by calibration experiments. Secondly, by the shade character of the front car, the different coordinates of the target car in the binocular images can be detected. Finally, a Secure-Vehicles- Spacing-Measurement(SVSM) program applied to expressway traffic was proposed, the distance between the target vehicle to the camera can be calculated by employing the common perpendicular midpoint method. The results show that the measurement program can measure a wide range of distance and can meet the needs of a SVSM for intelligent vehicles, which is an effective way for measuring the distance to the front car.
Following the research of the algorithms, a real-time image processing system based on TMS320DM642 was designed in this thesis. The system has the function of real-time dual channel video acquisition, real-time image processing, video output and so on. The system was applied in previous collision warning system and can carry out image processing in the frame-by-frame way, the processing time of one frame was 0.1s.
引文
[1]方平.汽车时代畅通城市道路问题研究及规划对策[D].武汉:武汉大学,2005.
[2]谢建平.基于多车协作感知的主动安全技术研究[D].上海:上海交通大学,2009.
[3]林芝.智能汽车的距离测量技术研究[J].汽车与安全,2002,3:44-47.
[4]刘燕.高速公路单目摄影纵向车距测量系统研究[D].陕西:长安大学,2002.
[5]Charles Thorpe, Martial H. Hebert and Takeo Kanade, Vision and Navigation for the Carnegie-Mellon Navlab[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL 10,NO 3,1988.05,362-373.
[6]T. Jochem. A portable navigation platform[J]. IEEE Symposium on Intelligent, Detroit, Michigan,1995:107-112.
[7]W. Huber. Autonomous Driving on Vehicle Test Tracks: Overview Motivation, and Concept Proc[J]. IEEE Conference Intelligent Vehicle,1998:445-450.
[8] Massimo Be rtozzi, Alberto Broggi.GOLD:A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection[C]. IEEE Transactions on Image Processing, 1998,7(1):62-81.
[9]Massimo Bertozzi, Alberto Broggi and Alessandra Fascioli. The ARGO autonomous vehicle’s and control systems[J]. International Journal of Intelligent Control and Systems, Vol.3,NO.4,1999:409-441.
[10]A. Broggi. Automatic Vehicle Guidance: The Experience of the ARGO Autonomous Vehicle[J]. World Science Publishing Co.Ltd,1999:25-32.
[11]李晗.基于机器视觉的高速公路车道标志线检测算法的研究[D].沈阳:东北大学,2006.
[12]刘化胜.智能车辆视觉导航中道路检测算法的研究[D].南京:南京航空航天大学,2004.
[13]郭磊,徐友春,李克强,连小珉.基于单目视觉的实时测距方法研究[J].中国图象图形学报, 2006,(01) .
[14]顾柏园.基于单目视觉的安全车距预警系统[D].吉林:吉林大学,2006.
[15]顾柏园,王荣本,余天洪,郭烈.基于视觉的前方车辆探测技术研究方法综述[J].公路交通科技, 2005,(10) .
[16]谢毅,黄席樾.基于机器视觉的车辆对称中心检测[J].重庆工学院学报(自然科学版),4,2007,21(4):8-11.
[17]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].中国图象图形学报,2003,8(5),590-595.
[18]叶艳芳.城区复杂背景条件下的车辆检测算法研究[D].重庆:重庆大学,2009.
[19]华冰.智能车辆中的道路检测和目标检测技术研究[D].重庆:重庆大学,2009.
[20]谢毅.基于DSP及机器视觉的道路识别与障碍物检测[D].重庆:重庆大学,2009.
[21]周俊杰.基于单目视觉的夜间车辆和车距检测[D].南京:南京理工大学, 2007.
[22]马伟.基于单个摄像机的车辆检测与跟踪[D].南京:南京理工大学,2008.
[23]曾素娣.基于单目视觉的测距算法研究[D].昆明:昆明理工大学,2007.
[24]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,2003.
[25]杨东.基于图像分割的立体匹配方法研究[D].北京:北京科技大学,2009.
[26]Marr D.Vision: A computational investigation into the human representation and processing of visual information. San Francisco: W.H.Freeman and Company,1982.
[27]王润生.图像理解[M].长沙:国防科技大学出版社,1998.
[28]章毓晋.图像工程(下册)图像理解(第二版)[M].北京:清华大学出版社,2007.
[29]韩九强.机器视觉技术及其应用[M].北京:高等教育出版社,2009.12.
[30]刘晶晶.基于双目立体视觉的三维定位技术研究[D].武汉:华中科技大学,2007.
[31]李洪海,王敬东.摄像机标定技术研究[J].光学仪器,2007,29(4):7-12.
[32]Roger Y.Tsai. A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the Shelf TV Cameras and Lenses[J].IEEE Journal of Robotics and Automation.1987,3(4):323-344.
[33]高立志,方勇,林志航等.高精度立体视觉测量中一种通用的摄像机标定技术[J].机械科学与技术,1998,17(5):808-811.
[34]曹宇,赵杰,李戈登.摄像机标定及恢复目标点世界坐标的一种方法[J].机械与电子,2008(6):3-6.
[35]程宏伟,黄文清,汪亚明.一种自动摄像机标定方法的实现[J].浙江理工大学学报,2007,24(3):262-265.
[36]熊志.基于双目立体视觉的大尺寸测量系统的研究[D].河北:燕山大学,2006.
[37]Zhang Z. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 26(10):44-48.
[38]Janne Heikkila,Olli Silven. A Four-step Camera Calibration Procedure with Implicit Image Correction[J]. Computer Vision and Pattern Recognition,1997: 1106-1112.
[39]Close-Range Camera Calibration - D.C. Brown, Photogrammetric Engineering, pages 855-866, Vol. 37, No. 8, 1971.
[40]李洪海.一种简便的双目摄像机标定方法[J].淮阴工学院学报,2009,18(3):56-61.
[41]李洪海.基于移动机器人的双目立体视觉技术研究[D].南京:南京航空航天大学,2007.
[42]胡铟,杨静宇.基于单目视觉的路面车辆检测及跟踪方法综述[J].公路交通科技2007, 24(12):127-131.
[43]ZEHANG SUN,BEBIS G,MILLER R.On-Road Vehicle Detection:A Review[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2006,28(5):694-711.
[44] D.Guo, T. Fraichard, M.Xie, and C.Laugier. Color modeling by spherical influence field in sensing driving environment, IEEE Intelligent Vehicle Symposium, 2000:249-254.
[45]谢毅.基于DSP及机器视觉的道路识别与障碍物检测[D].重庆:重庆大学,2007.
[46]赵庚.基于图像信息的被动测距关键技术研究[D].西安:西安电子科技大学,2009.
[47]孙振平,安向京,贺汉根.CITAVT-IV-视觉导航的自主车[J].机器人,2002, 24(2): 115-121.
[48]雷美琳,肖志涛,崔琴.基于双特征的前方车辆实时检测[J].天津师范大学学报(自然科学版),2010,30(1):23-26.
[49]HOFFMAN C,DANG T,STILLER C. Vehicle detection fusing 2D visual features[C].IEEE Proceedings of Intelligent Vehicles Symposium,2004:280-285.
[50]郭恩庆.基于视觉的高速公路前方车辆检测[D].吉林:吉林大学,2007.
[51]杨高波,杜青松.MATLAB图像/视频处理应用及实例[M].北京:电子工业出版社,2010.
[52]赵保佑.基于视觉的车辆检测与跟踪技术研究[D].武汉:武汉理工大学,2009.
[53]Stephane Raboisson and Philippe Schmouker. Obstacle Detection in Highway Environment by Colour CCD Camera and Image Processing Prototype Installed in a Vehicle. IEEE Trans. Pattern Recognition, Vol.18,No.4,1999,pp44~49.
[54]刘海波.基于双目立体视觉的移动机器人测障策略研究[D].河南:河南理工大学,2008.
[55]杨宁.基于边缘特征的前方车辆识别[D].吉林:吉林大学,2007.
[56]姬忠校,董惠.一种改进的基于数学形态学混合去噪方法[J].现代计算机(专业版), 2008,8:59-61.
[57]李立.基于机器视觉的运动车辆检测[D].兰州:兰州理工大学,2009.
[58]Illingworth J,Kittlor J.A survey of the Hough transform[J]. CVGIP, 1988, 44:87-116.
[59]游峰,王荣本,张荣辉等.基于单目视觉的区域交通智能车辆道路边界检测方法研究[J].公路交通科技,2008,25(6):135-143.
[60]King Hann LIM, Li-Minn ANG, Kah Phooi SENG et al. Lane-Vehicle Detection andTracking[J].Proceedings of the International MultiConference of Engineers and Computer Scientists, 2009, Vol 2.
[61]张广军.视觉测量[M].北京:科学出版社,2008.
[62]Y. Ruichek, H. Issa, J.G. Postaire et al. Towards real-time obstacle detection using a hierarchical decomposition methodology for stereo matching with a genetic algorithm. 16th IEEE International Conference, 2004:138-147.
[63]张钧,张宏,刘小茂等.双目立体视觉中物点定位的一种快速算法[J].信息与控制,2009,38(5):563-570.
[64]周福良.高速公路汽车被动测距系统的算法研究[D].浙江:浙江大学,2003.
[65]陈慧芳.高速公路汽车测距系统的研究[D].浙江;浙江大学,2004.
[66]Emanuele Trucco, Alessandro Verri. Introductory Techniques for 3-D Computer Vision[M]. Prentice Hall,1998.
[67]蔡光兴,李逢高.线性代数[M].北京:科学出版社,2007.
[68]马峰,李琼砚,赵亚东.异面直线公垂线中点算法为基的三维点重建[J].现代制造工程, 2009, 7:98-101.
[69]李克国,李大运,陶学恒.一种双目立体视觉技术中三维坐标点的改进求取算法[J].机电产品开发与创新,2008,21(5):31-35.
[70]张梅,张祖勋,张剑清等.空间点三维重建新方法及其不确定性研究[J].测绘科学,2008, 33(4) :8-11.
[71]王虹、刘嵩鹤、刘著铭.基于双目立体视觉安全车辆间距测量技术研究[J].交通与计算机,2007,25(6):90~93 . [72(]美)Texas Instruments Incorporated著,卞红雨,纪祥春,乔钢等编译.TMS320C6000系列DSP的CPU与外设[M].北京:清华大学出版社,2007. [73(]美)Texas Instruments Incorporated著,田黎育,何佩琨,朱梦宇编译.TMS320C6000系列DSP编程工具与指南[M].北京:清华大学出版社,2007.
[74] TMS320DM642 Video/Imaging Fixed Point Digital Signal Processor [M].Texas, USA: Texas Instruments,2004.
[75]Mohamed Aly. Real Time Detection of Lane Markers in Urban Streets[J]. IEEE Intelligent Vehicles Symposium,2008:7-12.
[76] OV7950 Datasheet [M].California, USA: Omnivision inc, 2006.
[77]王跃宗,刘京会.TMS320DM642应用系统设计与开发[M].北京:人民邮电出版社,2009.
[78]闫宗群,李刚,张维等.基于双TMS320DM642处理器的实时红外多目标图像处理系统[J].应用光学,2010,31(4):562-566.
[79]黄薇.视觉快速页面检测装置的研究[D].北京:北京工业大学,2009.
[80]李建科,黄席樾,杜长海登.基于TMS320DM642的嵌入式实时图像处理系统设计及应用[J].自动化与仪器仪表,2009,142(2):19-22.
[2]谢建平.基于多车协作感知的主动安全技术研究[D].上海:上海交通大学,2009.
[3]林芝.智能汽车的距离测量技术研究[J].汽车与安全,2002,3:44-47.
[4]刘燕.高速公路单目摄影纵向车距测量系统研究[D].陕西:长安大学,2002.
[5]Charles Thorpe, Martial H. Hebert and Takeo Kanade, Vision and Navigation for the Carnegie-Mellon Navlab[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL 10,NO 3,1988.05,362-373.
[6]T. Jochem. A portable navigation platform[J]. IEEE Symposium on Intelligent, Detroit, Michigan,1995:107-112.
[7]W. Huber. Autonomous Driving on Vehicle Test Tracks: Overview Motivation, and Concept Proc[J]. IEEE Conference Intelligent Vehicle,1998:445-450.
[8] Massimo Be rtozzi, Alberto Broggi.GOLD:A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection[C]. IEEE Transactions on Image Processing, 1998,7(1):62-81.
[9]Massimo Bertozzi, Alberto Broggi and Alessandra Fascioli. The ARGO autonomous vehicle’s and control systems[J]. International Journal of Intelligent Control and Systems, Vol.3,NO.4,1999:409-441.
[10]A. Broggi. Automatic Vehicle Guidance: The Experience of the ARGO Autonomous Vehicle[J]. World Science Publishing Co.Ltd,1999:25-32.
[11]李晗.基于机器视觉的高速公路车道标志线检测算法的研究[D].沈阳:东北大学,2006.
[12]刘化胜.智能车辆视觉导航中道路检测算法的研究[D].南京:南京航空航天大学,2004.
[13]郭磊,徐友春,李克强,连小珉.基于单目视觉的实时测距方法研究[J].中国图象图形学报, 2006,(01) .
[14]顾柏园.基于单目视觉的安全车距预警系统[D].吉林:吉林大学,2006.
[15]顾柏园,王荣本,余天洪,郭烈.基于视觉的前方车辆探测技术研究方法综述[J].公路交通科技, 2005,(10) .
[16]谢毅,黄席樾.基于机器视觉的车辆对称中心检测[J].重庆工学院学报(自然科学版),4,2007,21(4):8-11.
[17]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].中国图象图形学报,2003,8(5),590-595.
[18]叶艳芳.城区复杂背景条件下的车辆检测算法研究[D].重庆:重庆大学,2009.
[19]华冰.智能车辆中的道路检测和目标检测技术研究[D].重庆:重庆大学,2009.
[20]谢毅.基于DSP及机器视觉的道路识别与障碍物检测[D].重庆:重庆大学,2009.
[21]周俊杰.基于单目视觉的夜间车辆和车距检测[D].南京:南京理工大学, 2007.
[22]马伟.基于单个摄像机的车辆检测与跟踪[D].南京:南京理工大学,2008.
[23]曾素娣.基于单目视觉的测距算法研究[D].昆明:昆明理工大学,2007.
[24]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,2003.
[25]杨东.基于图像分割的立体匹配方法研究[D].北京:北京科技大学,2009.
[26]Marr D.Vision: A computational investigation into the human representation and processing of visual information. San Francisco: W.H.Freeman and Company,1982.
[27]王润生.图像理解[M].长沙:国防科技大学出版社,1998.
[28]章毓晋.图像工程(下册)图像理解(第二版)[M].北京:清华大学出版社,2007.
[29]韩九强.机器视觉技术及其应用[M].北京:高等教育出版社,2009.12.
[30]刘晶晶.基于双目立体视觉的三维定位技术研究[D].武汉:华中科技大学,2007.
[31]李洪海,王敬东.摄像机标定技术研究[J].光学仪器,2007,29(4):7-12.
[32]Roger Y.Tsai. A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the Shelf TV Cameras and Lenses[J].IEEE Journal of Robotics and Automation.1987,3(4):323-344.
[33]高立志,方勇,林志航等.高精度立体视觉测量中一种通用的摄像机标定技术[J].机械科学与技术,1998,17(5):808-811.
[34]曹宇,赵杰,李戈登.摄像机标定及恢复目标点世界坐标的一种方法[J].机械与电子,2008(6):3-6.
[35]程宏伟,黄文清,汪亚明.一种自动摄像机标定方法的实现[J].浙江理工大学学报,2007,24(3):262-265.
[36]熊志.基于双目立体视觉的大尺寸测量系统的研究[D].河北:燕山大学,2006.
[37]Zhang Z. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 26(10):44-48.
[38]Janne Heikkila,Olli Silven. A Four-step Camera Calibration Procedure with Implicit Image Correction[J]. Computer Vision and Pattern Recognition,1997: 1106-1112.
[39]Close-Range Camera Calibration - D.C. Brown, Photogrammetric Engineering, pages 855-866, Vol. 37, No. 8, 1971.
[40]李洪海.一种简便的双目摄像机标定方法[J].淮阴工学院学报,2009,18(3):56-61.
[41]李洪海.基于移动机器人的双目立体视觉技术研究[D].南京:南京航空航天大学,2007.
[42]胡铟,杨静宇.基于单目视觉的路面车辆检测及跟踪方法综述[J].公路交通科技2007, 24(12):127-131.
[43]ZEHANG SUN,BEBIS G,MILLER R.On-Road Vehicle Detection:A Review[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2006,28(5):694-711.
[44] D.Guo, T. Fraichard, M.Xie, and C.Laugier. Color modeling by spherical influence field in sensing driving environment, IEEE Intelligent Vehicle Symposium, 2000:249-254.
[45]谢毅.基于DSP及机器视觉的道路识别与障碍物检测[D].重庆:重庆大学,2007.
[46]赵庚.基于图像信息的被动测距关键技术研究[D].西安:西安电子科技大学,2009.
[47]孙振平,安向京,贺汉根.CITAVT-IV-视觉导航的自主车[J].机器人,2002, 24(2): 115-121.
[48]雷美琳,肖志涛,崔琴.基于双特征的前方车辆实时检测[J].天津师范大学学报(自然科学版),2010,30(1):23-26.
[49]HOFFMAN C,DANG T,STILLER C. Vehicle detection fusing 2D visual features[C].IEEE Proceedings of Intelligent Vehicles Symposium,2004:280-285.
[50]郭恩庆.基于视觉的高速公路前方车辆检测[D].吉林:吉林大学,2007.
[51]杨高波,杜青松.MATLAB图像/视频处理应用及实例[M].北京:电子工业出版社,2010.
[52]赵保佑.基于视觉的车辆检测与跟踪技术研究[D].武汉:武汉理工大学,2009.
[53]Stephane Raboisson and Philippe Schmouker. Obstacle Detection in Highway Environment by Colour CCD Camera and Image Processing Prototype Installed in a Vehicle. IEEE Trans. Pattern Recognition, Vol.18,No.4,1999,pp44~49.
[54]刘海波.基于双目立体视觉的移动机器人测障策略研究[D].河南:河南理工大学,2008.
[55]杨宁.基于边缘特征的前方车辆识别[D].吉林:吉林大学,2007.
[56]姬忠校,董惠.一种改进的基于数学形态学混合去噪方法[J].现代计算机(专业版), 2008,8:59-61.
[57]李立.基于机器视觉的运动车辆检测[D].兰州:兰州理工大学,2009.
[58]Illingworth J,Kittlor J.A survey of the Hough transform[J]. CVGIP, 1988, 44:87-116.
[59]游峰,王荣本,张荣辉等.基于单目视觉的区域交通智能车辆道路边界检测方法研究[J].公路交通科技,2008,25(6):135-143.
[60]King Hann LIM, Li-Minn ANG, Kah Phooi SENG et al. Lane-Vehicle Detection andTracking[J].Proceedings of the International MultiConference of Engineers and Computer Scientists, 2009, Vol 2.
[61]张广军.视觉测量[M].北京:科学出版社,2008.
[62]Y. Ruichek, H. Issa, J.G. Postaire et al. Towards real-time obstacle detection using a hierarchical decomposition methodology for stereo matching with a genetic algorithm. 16th IEEE International Conference, 2004:138-147.
[63]张钧,张宏,刘小茂等.双目立体视觉中物点定位的一种快速算法[J].信息与控制,2009,38(5):563-570.
[64]周福良.高速公路汽车被动测距系统的算法研究[D].浙江:浙江大学,2003.
[65]陈慧芳.高速公路汽车测距系统的研究[D].浙江;浙江大学,2004.
[66]Emanuele Trucco, Alessandro Verri. Introductory Techniques for 3-D Computer Vision[M]. Prentice Hall,1998.
[67]蔡光兴,李逢高.线性代数[M].北京:科学出版社,2007.
[68]马峰,李琼砚,赵亚东.异面直线公垂线中点算法为基的三维点重建[J].现代制造工程, 2009, 7:98-101.
[69]李克国,李大运,陶学恒.一种双目立体视觉技术中三维坐标点的改进求取算法[J].机电产品开发与创新,2008,21(5):31-35.
[70]张梅,张祖勋,张剑清等.空间点三维重建新方法及其不确定性研究[J].测绘科学,2008, 33(4) :8-11.
[71]王虹、刘嵩鹤、刘著铭.基于双目立体视觉安全车辆间距测量技术研究[J].交通与计算机,2007,25(6):90~93 . [72(]美)Texas Instruments Incorporated著,卞红雨,纪祥春,乔钢等编译.TMS320C6000系列DSP的CPU与外设[M].北京:清华大学出版社,2007. [73(]美)Texas Instruments Incorporated著,田黎育,何佩琨,朱梦宇编译.TMS320C6000系列DSP编程工具与指南[M].北京:清华大学出版社,2007.
[74] TMS320DM642 Video/Imaging Fixed Point Digital Signal Processor [M].Texas, USA: Texas Instruments,2004.
[75]Mohamed Aly. Real Time Detection of Lane Markers in Urban Streets[J]. IEEE Intelligent Vehicles Symposium,2008:7-12.
[76] OV7950 Datasheet [M].California, USA: Omnivision inc, 2006.
[77]王跃宗,刘京会.TMS320DM642应用系统设计与开发[M].北京:人民邮电出版社,2009.
[78]闫宗群,李刚,张维等.基于双TMS320DM642处理器的实时红外多目标图像处理系统[J].应用光学,2010,31(4):562-566.
[79]黄薇.视觉快速页面检测装置的研究[D].北京:北京工业大学,2009.
[80]李建科,黄席樾,杜长海登.基于TMS320DM642的嵌入式实时图像处理系统设计及应用[J].自动化与仪器仪表,2009,142(2):19-22.
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