交通信息处理系统的研究和实现
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摘要
智能交通系统(ITS)作为机器视觉的车辆和车型分类技术,是发展现代化交通的必由之路。研究智能交通系统是国家发展经济的一个重要方向,它在高速公路收费系统和道路交通监控系统等方面都有着广泛的应用前景。本文依据这样的发点,对智能交通系统中的车辆检测、提取和分类技术进行了深入地研究和分析,并提出了一种精确的车辆检测分类算法,主要内容如下:
首先利用现有的数字图像处理相关知识对图像进行预处理,减少噪声对结果的影响,对比分析目前现有的抑噪声算法,提出一种改进的滤波抑噪声算法。同时提出一种阀值分割算法对车辆进行检测提取,该算法采用动态调整阀值能对背景进行自动更新。接着基于视频图像方面知识,分析如何利用采集卡进行图像采集,分析。其次介绍了视频检测技术的方法和用图像处理和识别技术对目标进行检测和识别的原理,并根据智能交通控制与仿真对于交通流量、车辆速度、车辆到达率等交通检测的需求,基于视频图像进行了交通信息处理的算法研究与实现。最后,通过对图像滤波除噪、图像增强、图像分割、图像锐化、特征提取与目标识别等图像处理过程中遇到的这些关键问题分析,在此基础上确定了机动车辆违章检测系统设计和实现中所要采用的背景相减法数学模型,完成了背景自动更新。
本文提出的智能交通系统适用于大面积、多目标的复杂场景,能有效排除干扰,满足自然条件下视频监控的车辆检测和车型分类要求,具有一定的理论意义和实用价值,可以推广应用到视频监控的其它领域中。
Intelligent Transport Systems(ITS) is the determinate approaches for the development of the modern transports,the vehicle detection and classification based on computer vision that is a important research field for the advance of the ITS。It has promising prospect in the application of the road traffic surveillance system and the highway toll system and so on. In this thesis,we research deeply the technique about vehicle detection and classification,and propose a precise and robust algorithm of detecting and classifying moving vehicles on road. The main contents can be listed as follows:
This thesis designs the system of traffic information detection according to the need of traffic information detection. This thesis includes the constitutes of system hardware, image pretreatment, the achievement of parameter detection artithmetic. Secondly, the dissertation introducedmethod of video-detecting technology and principle of detecting and recognition with image-processing, such as image filtering and noise reduction,image sharpening, image enhancement, image segmentation, feature pick-up and boject recognition. On the basis of these analysis, the dissertation set up a mathematical model of background subtraction method adopted in design and realization of motor vehicle peccancy-detecting system and accomplished automatic update of background.
The methods proposed in this thesis adapt to complex seenes such as large area and multiple objects, and can satisfy the requirement of vehicle detection and classification in natural environment. The research would be reasonable and valuable in the oretical and practical areas,and can be generalized to other fields of video surveillance.
首先利用现有的数字图像处理相关知识对图像进行预处理,减少噪声对结果的影响,对比分析目前现有的抑噪声算法,提出一种改进的滤波抑噪声算法。同时提出一种阀值分割算法对车辆进行检测提取,该算法采用动态调整阀值能对背景进行自动更新。接着基于视频图像方面知识,分析如何利用采集卡进行图像采集,分析。其次介绍了视频检测技术的方法和用图像处理和识别技术对目标进行检测和识别的原理,并根据智能交通控制与仿真对于交通流量、车辆速度、车辆到达率等交通检测的需求,基于视频图像进行了交通信息处理的算法研究与实现。最后,通过对图像滤波除噪、图像增强、图像分割、图像锐化、特征提取与目标识别等图像处理过程中遇到的这些关键问题分析,在此基础上确定了机动车辆违章检测系统设计和实现中所要采用的背景相减法数学模型,完成了背景自动更新。
本文提出的智能交通系统适用于大面积、多目标的复杂场景,能有效排除干扰,满足自然条件下视频监控的车辆检测和车型分类要求,具有一定的理论意义和实用价值,可以推广应用到视频监控的其它领域中。
Intelligent Transport Systems(ITS) is the determinate approaches for the development of the modern transports,the vehicle detection and classification based on computer vision that is a important research field for the advance of the ITS。It has promising prospect in the application of the road traffic surveillance system and the highway toll system and so on. In this thesis,we research deeply the technique about vehicle detection and classification,and propose a precise and robust algorithm of detecting and classifying moving vehicles on road. The main contents can be listed as follows:
This thesis designs the system of traffic information detection according to the need of traffic information detection. This thesis includes the constitutes of system hardware, image pretreatment, the achievement of parameter detection artithmetic. Secondly, the dissertation introducedmethod of video-detecting technology and principle of detecting and recognition with image-processing, such as image filtering and noise reduction,image sharpening, image enhancement, image segmentation, feature pick-up and boject recognition. On the basis of these analysis, the dissertation set up a mathematical model of background subtraction method adopted in design and realization of motor vehicle peccancy-detecting system and accomplished automatic update of background.
The methods proposed in this thesis adapt to complex seenes such as large area and multiple objects, and can satisfy the requirement of vehicle detection and classification in natural environment. The research would be reasonable and valuable in the oretical and practical areas,and can be generalized to other fields of video surveillance.
引文
[1]章毓晋,图象工程(上、下),清华大学出版社,2002。
[2] K.R.Castleman: Digital Image Processing, Prentice Hall, Inc,1996.
[3] Atsushi Saito, Masatoshi Kinachi, Shiro ogata: Silhouette Vision: New Video Vehicle Detection Field Proven Robust and Accurate Proceeding of 6th World Congress on Intelligent Transport Systems, Tornoto, Canada,1999
[4] A.Bensrhair, M.Bertozzi, A.Broggi, P.Mice, S.Mousset, and G.Toulminet: A Cooperative Approach to Visionbased Vehicle Detection IEEE Intelligent Transportation Systems Conference Proceeding, Oakland(CA) USA August 25-29 2001 Page 207-212
[5] Dieter Koller, Joseph Weber, and Jitendra Malik Robust Multiple Car Tracking with Occlusion Reasoning University of Califormia at Berkeley, EECS Dept,Berkeley, CA 94720,USA.
[6] M.Kass, A.Witkin, D.Terzoopoulos,Snakes:Active Contour Models, International Journal of Computer Vision1()1988 Page 321-331.
[7] R.Bartel,J.beatty, B.Barskt, An introduction to Splines for use in Computer Vision, Morgan Kaufman, 1987
[8]马骥等,基于视频图像的实时车道探测算法研究,第二届海峡两岸智能运输系统学术研讨会论文集,2002。
[9]隋哗等,基于视觉的运动目标分类和跟踪研究,第二届海峡两岸智能运输系统学术研讨会论文集,2002。
[10]洪文斌等,实时驾驶人疲倦侦测系统,第二届海峡两岸智能运输系统学术研讨会论文集,2002。
[11]陈家棋等,运动图像处理在汽车车型识别中的应用,汽车工程,第6期,1998。
[12]魏武等,基于计算机视觉和图像处理的交通参数检测,信息与控制,第3期,2001。
[13]郭旭平等,跟踪系统中图像目标的快速提取技术研究,电子科技大学学报,第l期,1998。
[14]李在铭.数字图像处理.压缩与识别技术.成都:电子科技大学出版社,2000
[15]姚健超,宣国荣.基于图像处理的实时交通流检测系统.交通与计算机.1991
[16]王磊,王为民.模糊控制理论及应用。北京:国防工业出版社,1997 [17 ]崔屹.图像处理与分析数学形态学方法。北京:科学工业出版社,2000
[18]张乃尧,阎平凡.神经网络与模糊控制。北京:清华大学出版社,1998
[19] KemlhetR.Castlmena著,朱志刚等译,数字图像处理[M]北京:电子工业出版社,200.22
[20]徐建华.图像处理与分析[M]北京:科学出版社,,199.25
[21]夏良正,数字图像处理[M]南京:东南大学出版社,1999.06
[22]李智勇等,动态图像分析[M]北京:国防工业出版社,1999.05
[23] Jun-Wei Hsieh,Shih-Hao Yu,Yung-Sheng Chen.Morphology-based license plate detection from complex scenes.Pattern Recognition,2002. Proceedings.16th International Conference on 2002.Aug.Vol.3
[24] Christ of Ridder,Munkelt,and Harald Kirchner.Adaptive BackgroundEstimation and Foreground Detectin using kalman-Filtering.Proceedings of International Conference on recent Advances in Mechatronics,ICRAM'95UNESCO Chair on Mechatronics,193-199,1995.
[25]张宏林.数字图像模式识别技术及工程实践机械工业出版社,2000.3
[26]章毓晋.图像工程(上下册)图像处理与分析。北京:清华大学出版社,2000
[27] Shunsuke Kamijo,Yasuyuki Matsushita . Traffic Monitoring and Accident Detection at Intersections.IEEE,2000
[28] George S.K.Fung,Nelson H.C.Yung。Vehicle Shape Approximation from Motion for Visual Traffic Surveillance。IEEE,2001
[29]高大山.智能交通系统中运动目标检测方法的研究.清华大学硕士学位论文,2002.
[30] CueehiaraR,GranaC,PiceardiM,etal. Improving shadow suppression in moving objeet deteetion with HSV color information. Proeeedings of IEEE Intelligent Transportation Systems Conferenee,2001: 334-339.
[31]王运琼.车辆识别系统中几个关键技术的研究.四川大学博士论文,200.4
[2] K.R.Castleman: Digital Image Processing, Prentice Hall, Inc,1996.
[3] Atsushi Saito, Masatoshi Kinachi, Shiro ogata: Silhouette Vision: New Video Vehicle Detection Field Proven Robust and Accurate Proceeding of 6th World Congress on Intelligent Transport Systems, Tornoto, Canada,1999
[4] A.Bensrhair, M.Bertozzi, A.Broggi, P.Mice, S.Mousset, and G.Toulminet: A Cooperative Approach to Visionbased Vehicle Detection IEEE Intelligent Transportation Systems Conference Proceeding, Oakland(CA) USA August 25-29 2001 Page 207-212
[5] Dieter Koller, Joseph Weber, and Jitendra Malik Robust Multiple Car Tracking with Occlusion Reasoning University of Califormia at Berkeley, EECS Dept,Berkeley, CA 94720,USA.
[6] M.Kass, A.Witkin, D.Terzoopoulos,Snakes:Active Contour Models, International Journal of Computer Vision1()1988 Page 321-331.
[7] R.Bartel,J.beatty, B.Barskt, An introduction to Splines for use in Computer Vision, Morgan Kaufman, 1987
[8]马骥等,基于视频图像的实时车道探测算法研究,第二届海峡两岸智能运输系统学术研讨会论文集,2002。
[9]隋哗等,基于视觉的运动目标分类和跟踪研究,第二届海峡两岸智能运输系统学术研讨会论文集,2002。
[10]洪文斌等,实时驾驶人疲倦侦测系统,第二届海峡两岸智能运输系统学术研讨会论文集,2002。
[11]陈家棋等,运动图像处理在汽车车型识别中的应用,汽车工程,第6期,1998。
[12]魏武等,基于计算机视觉和图像处理的交通参数检测,信息与控制,第3期,2001。
[13]郭旭平等,跟踪系统中图像目标的快速提取技术研究,电子科技大学学报,第l期,1998。
[14]李在铭.数字图像处理.压缩与识别技术.成都:电子科技大学出版社,2000
[15]姚健超,宣国荣.基于图像处理的实时交通流检测系统.交通与计算机.1991
[16]王磊,王为民.模糊控制理论及应用。北京:国防工业出版社,1997 [17 ]崔屹.图像处理与分析数学形态学方法。北京:科学工业出版社,2000
[18]张乃尧,阎平凡.神经网络与模糊控制。北京:清华大学出版社,1998
[19] KemlhetR.Castlmena著,朱志刚等译,数字图像处理[M]北京:电子工业出版社,200.22
[20]徐建华.图像处理与分析[M]北京:科学出版社,,199.25
[21]夏良正,数字图像处理[M]南京:东南大学出版社,1999.06
[22]李智勇等,动态图像分析[M]北京:国防工业出版社,1999.05
[23] Jun-Wei Hsieh,Shih-Hao Yu,Yung-Sheng Chen.Morphology-based license plate detection from complex scenes.Pattern Recognition,2002. Proceedings.16th International Conference on 2002.Aug.Vol.3
[24] Christ of Ridder,Munkelt,and Harald Kirchner.Adaptive BackgroundEstimation and Foreground Detectin using kalman-Filtering.Proceedings of International Conference on recent Advances in Mechatronics,ICRAM'95UNESCO Chair on Mechatronics,193-199,1995.
[25]张宏林.数字图像模式识别技术及工程实践机械工业出版社,2000.3
[26]章毓晋.图像工程(上下册)图像处理与分析。北京:清华大学出版社,2000
[27] Shunsuke Kamijo,Yasuyuki Matsushita . Traffic Monitoring and Accident Detection at Intersections.IEEE,2000
[28] George S.K.Fung,Nelson H.C.Yung。Vehicle Shape Approximation from Motion for Visual Traffic Surveillance。IEEE,2001
[29]高大山.智能交通系统中运动目标检测方法的研究.清华大学硕士学位论文,2002.
[30] CueehiaraR,GranaC,PiceardiM,etal. Improving shadow suppression in moving objeet deteetion with HSV color information. Proeeedings of IEEE Intelligent Transportation Systems Conferenee,2001: 334-339.
[31]王运琼.车辆识别系统中几个关键技术的研究.四川大学博士论文,200.4