基于单个摄像机的车辆检测与跟踪
|
摘要
本文的研究内容是基于单个摄像机的车辆检测与跟踪,主要是对本车前方的运动车辆进行检测和跟踪。车辆检测一般分为两个步骤:第一步,找到车辆的候选区域;第二步,对车辆候选区域进行确认。本文首先详细分析了前方车辆在图像中所具有的一些特征,最后融合了车辆底部的阴影、纹理、形状、和灰度对称特征进行车辆的检测。通过车辆底部的阴影找到感兴趣区域ROI(Region Of Interesting),然后运用熵初步过滤掉一些路面干扰区域,剩下的区域作为车辆检测的候选区域,完成车辆检测的第一步。由于模型匹配方法过分依赖模型,在模型匹配失败的时候无法完成车辆的检测,而且很难为车辆建立一个精确的模型,所以本文融合车辆的形状特征和灰度对称特征对车辆的候选区域进行确认,完成车辆检测的第二步。从实验结果看,本文所提出的多种特征融合的车辆检测方法能对前方不同形状、颜色、大小、距离的车辆进行检测,也能对不同姿态的车辆进行检测。而且本文的车辆检测算法受光照的影响小,能在一定程度上完成由于车辆的颠簸造成摄像机抖动情况下的车辆检测,也能完成复杂路况下的车辆检测,是一种非常有效的车辆检测方法。
为了提高车辆检测算法的鲁棒性和减少算法的时间复杂度,车辆检测过后一般都需要对检测出的车辆进行跟踪。在对车辆进行跟踪的时候,本文首先分析了基于模型的运动车辆跟踪方法和基于Kalman滤波器的运动车辆跟踪方法。由于基于模型的运动车辆跟踪方法在进行车辆跟踪的时容易发生跟踪漂移,而且不能处理遮挡情况下的跟踪:基于Kalman滤波器的运动车辆跟踪方法一般都是假设车辆做匀速或者匀加速直线运动,对于实际车辆的运动规律不是很恰当,而且还存在Kalman滤波器参数初始化等问题,跟踪结果也不好。本文提出了基于模型和灰度对称融合的运动车辆跟踪方法。从实验结果看,基于模型和灰度对称融合的运动车辆跟踪方法能稳定的跟踪到前方远距离车辆,能适应光照剧烈改变情况下的车辆跟踪,能处理遮挡问题,是一种有效的车辆跟踪方法。
The research content of this paper is vehicle detection and tracking based on a single camera. It is mainly to detect and track front vehicles. In general, vehicle detection is divided into two steps. The first step is to find vehicle candidate regions; the second step is to verify the vehicle candidate regions. In this paper, we firstly analyze the front vehicle's features in detail, and then we fuse vehicle's multiple features to detect a vehicle. The fused features include: shadow underneath the vehicle, texture, shape and gray-scale symmetry. According to find the shadow underneath a vehicle, we can get some ROI(Region Of Interesting), and then we use entropy to remove some disturbing regions which lack of entropy. The left regions are regarded as vehicle candidate regions. After above process, we finish the first step of vehicle detection. As model based method relies too much on model, it can not detect vehicles when model matches unsuccessfully. Another problem is that it is very difficult to establish a precise model for a vehicle. So we do not use model based method to detect a vehicle. We synchronously fuse vehicle's shape and gray-scale symmetry features to verify vehicle candidate regions. After above process, we finish the second step of vehicle detection. So far, vehicle detection has finished after these two steps. Our experiment results show that vehicle detection based on fusion of multiple features proposed in this paper can detect front vehicles with different shape, color, size, distance and gesture. This method is affected little by light condition, and can detect vehicles to some extent when camera shocks. It also can detect vehicles under different complex road environments. The vehicle detection method proposed in this paper is an effective method.
In order to improve the robustness of vehicle detection and reduce time consuming, it always needs to track the detected vehicles after vehicle detection step. In vehicle tracking step, we firstly use model based vehicle tracking method and kalman filter based vehicle tracking method to track the detected vehicles. The experiment results of these two methods are not good. Model based vehicle tracking method will cause tracking drift, and can not deal with occlusion; Kalman filter based vehicle tracking method generally assumes that the motion of vehicle is uniformly linear motion or uniformly accelerated linear motion. This assumption is not appropriate for the actual movement of vehicles, and there are still problems about initializing the parameters of kalman filter. For the above reasons, we propose a vehicle tracking method called vehicle tracking based on fusion of model and gray-scale symmetry. The experiment results show that the proposed method can stably track front vehicles with long distance. It can adapt the condition that light changes drastically, and can deal with occlusion. Vehicle tracking based on fusion of model and gray-scale symmetry is an effective vehicle tracking method.
为了提高车辆检测算法的鲁棒性和减少算法的时间复杂度,车辆检测过后一般都需要对检测出的车辆进行跟踪。在对车辆进行跟踪的时候,本文首先分析了基于模型的运动车辆跟踪方法和基于Kalman滤波器的运动车辆跟踪方法。由于基于模型的运动车辆跟踪方法在进行车辆跟踪的时容易发生跟踪漂移,而且不能处理遮挡情况下的跟踪:基于Kalman滤波器的运动车辆跟踪方法一般都是假设车辆做匀速或者匀加速直线运动,对于实际车辆的运动规律不是很恰当,而且还存在Kalman滤波器参数初始化等问题,跟踪结果也不好。本文提出了基于模型和灰度对称融合的运动车辆跟踪方法。从实验结果看,基于模型和灰度对称融合的运动车辆跟踪方法能稳定的跟踪到前方远距离车辆,能适应光照剧烈改变情况下的车辆跟踪,能处理遮挡问题,是一种有效的车辆跟踪方法。
The research content of this paper is vehicle detection and tracking based on a single camera. It is mainly to detect and track front vehicles. In general, vehicle detection is divided into two steps. The first step is to find vehicle candidate regions; the second step is to verify the vehicle candidate regions. In this paper, we firstly analyze the front vehicle's features in detail, and then we fuse vehicle's multiple features to detect a vehicle. The fused features include: shadow underneath the vehicle, texture, shape and gray-scale symmetry. According to find the shadow underneath a vehicle, we can get some ROI(Region Of Interesting), and then we use entropy to remove some disturbing regions which lack of entropy. The left regions are regarded as vehicle candidate regions. After above process, we finish the first step of vehicle detection. As model based method relies too much on model, it can not detect vehicles when model matches unsuccessfully. Another problem is that it is very difficult to establish a precise model for a vehicle. So we do not use model based method to detect a vehicle. We synchronously fuse vehicle's shape and gray-scale symmetry features to verify vehicle candidate regions. After above process, we finish the second step of vehicle detection. So far, vehicle detection has finished after these two steps. Our experiment results show that vehicle detection based on fusion of multiple features proposed in this paper can detect front vehicles with different shape, color, size, distance and gesture. This method is affected little by light condition, and can detect vehicles to some extent when camera shocks. It also can detect vehicles under different complex road environments. The vehicle detection method proposed in this paper is an effective method.
In order to improve the robustness of vehicle detection and reduce time consuming, it always needs to track the detected vehicles after vehicle detection step. In vehicle tracking step, we firstly use model based vehicle tracking method and kalman filter based vehicle tracking method to track the detected vehicles. The experiment results of these two methods are not good. Model based vehicle tracking method will cause tracking drift, and can not deal with occlusion; Kalman filter based vehicle tracking method generally assumes that the motion of vehicle is uniformly linear motion or uniformly accelerated linear motion. This assumption is not appropriate for the actual movement of vehicles, and there are still problems about initializing the parameters of kalman filter. For the above reasons, we propose a vehicle tracking method called vehicle tracking based on fusion of model and gray-scale symmetry. The experiment results show that the proposed method can stably track front vehicles with long distance. It can adapt the condition that light changes drastically, and can deal with occlusion. Vehicle tracking based on fusion of model and gray-scale symmetry is an effective vehicle tracking method.
引文
[1]Willie D.Jones.Keeping Cars from Crashing.IEEE Spectrum.2001,38(9):40-45.
[2]http://www.mps.gov.cn/cenweb/brjlCenweb/jsp/common/article.jsp?infoid=ABC00000000000042969.
[3]袁泉.基于“以人为中心”的汽车安全性研究.汽车安全.1998(6):37-39.
[4]熊和金.智能汽车系统研究的若干问题.交通运输工程学报.2001,1(2):37-40.
[5]Richard Bishop.Intelligent vehicle applications worldwide.IEEE Intelligent Systems.2000,15(1):78-81.
[6]李斌,王荣本,郭克友.基于机器视觉的智能车辆障碍物检测方法研究.公路交通科技.2002,19(4):126-129.
[7]Zehang Sun,George Bebis,Ronald Miller.On-Road Vehicle Detection:A Review.IEEE Transactions on Pattern Analysis and Machine Intelligence.2006,28(5):694-711.
[8]Hideo Mori,N.Moghadam Charkari.Shadow and Rhythm as Sign patterns of Obstacle Detection.IEEE International Symposium on Industrial Electronics.1993:271-277.
[9]T.K.ten Kate,M.B.van Leewen,S.E.Moro-Ellenberger,B.J.F.Driessen,A.H.G.Versluis,F.C.A.Groen.Mid-range and Distant Vehicle Detection with a Mobile Camera.IEEE Intelligent Vehicles Symposium.2004:72-77.
[10]Clady X.,Collange F,Jurie F.,Martinet P..Cars detection and tracking with a vision sensor.IEEE Intelligent Vehicles Symposium.2003:593-598.
[11]Sam Yong Kim,Se-Young Oh,JeongKwan Kang,Young Woo Ryu,Kwangsoo Kim,Sang-Cheol Park,KyongHa Park.Front and Rear Vehicle Detection and Tracking in the Day and Night Times Using Vision and Sonar Sensor Fusion.IEEE/RSJ International Conference on Intelligent Robots and Systems.2005:2173-2178.
[12]Shih-Shinh Huang,Chung-Jen Chen,Pei-Yung Hsiao,Li-Chen Fu.On-Board Vision System for Lane Recognition and Front-Vehicle Detection to Enhance Driver's Awareness.IEEE International Conference on Robotics & Automation.2004:2456-2461.
[13]Tie Liu,Nanning Zheng,Li Zhao,Hong Cheng.Learning based Symmetric Features Selection for Vehicle Detection.IEEE Intelligent Vehicles Symposium.2005:124-129.
[14]Thomas Zielke,Michael Brauckmann,Wemer von Seelen.Intensity and Edge-based Symmetry Detection with an Application to Car-following.Computer Vision,Graphics,and Image Processing:Image Understanding.1993,58(2):177-190.
[15]Chu Jiangwei,Ji Lisheng,Guo Lie,Libibing,Wang Rongben.Study on Method of Detecting Preceding Vehicle Based on Monocular Camera.IEEE Intelligent Vehicles Symposium.2004:750-755.
[16]A.Bensrhair,M.Bertozzi,A.Broggi,P.Mich.A Cooperative Approach to Vision-based Vehicle Detection.IEEE Intelligent Transportation Systems.2001:207-212.
[17]陈慧芳,严惠民,姚晓强.被动测距系统汽车目标提取算法研究.浙江大学学报(工学版).2005,39(4):526-529.
[18]范培蕾,张晓今,廖传锦,寇保华.基于视觉的高速公路目标检测与跟踪系统研究.重庆邮电学院学报(自然科学版).2005,17(6):776-779.
[19]Massimo Bertozzi,Alberto Broggi,Stefano Castelluccio.A real-time oriented system for vehicle detection.Journal of Systems Architecture.1997:317-325.
[20]周福良,严惠民.高速公路汽车测距系统中目标汽车提取的研究.公路交通科技.2003,20(5):87-90.
[21]刘勃,周荷琴,魏铭旭.基于颜色和运动信息的夜间车辆检测方法.中国图象图形学报.2005,10(2):187-191.
[22]Thomas Kalinke,Christos Tzomakas,Werner v.Seelen.A Texture-based Object Detection and an Adaptive Model-Based Classification.IEEE International Conference on Intelligent Vehicles.1998:143-148.
[23]Uwe Handmann and Thomas Kalinke.Fusion of Texture and Contour Based Methods for Object Recognition.IEEE Intelligent Transportation System.1997:876-881.
[24]吴珺文,张学工.应用小波变换和PCA进行车辆的静态图像检测.清华大学学报(自然科学版).2002,42(11):1560-1564.
[25]Zehang Sun,Ronald Miller,George Bebis,David DiMeo.A Real-time Precrash Vehicle Detection System.IEEE Workshop on Applications of Computer Vision.2002:171-176.
[26]Zehang Sun,George Bebis,Ronald Miler.On-Road Vehicle Detection Using Gabor Filters and Support Vector Machines.International Conference on Digital Signal Processing.2002,2:1019-1022.
[27]Berthold K.P.Horn,Brian G.Schunck.Determining Optical Flow.Artificial Intelligence.1981,17(123):185-204.
[28]Parag H.Batavia,Dean A.Pomerleau,Charles E.Thorpe.Overtaking Vehicle Detection Using Implicit Optical Flow.IEEE Conference on Intelligent Transportation System.1997:729-734.
[29]Junxian Wang,George Bebis,Ronald Miller.Overtaking Vehicle Detection Using Dynamic and Quasi-Static Background Modeling.IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2005,3:64-64.
[30]Lefaix G.,Marchand T.,Bouthemy P..Motion-based Obstacle Detection and Tracking for Car Driving Assistance.IEEE International Conference on Pattern Recognition.2002,4:74-77.
[31]ZENG Zhi-Hong.Lane Detection and Car Tracking on the Highway.自动化学报.2003,29(3):450-456.
[32]J.M.Collado,C.Hilario,A.de la Escalera,J.M Armingol.Model Based Vehicle Detection for Intelligent Vehicles.IEEE Intelligent Vehicles Symposium.2004:572-577.
[33]胡铟,杨静宇.基于模型的车辆检测及跟踪.中国图象图形学报.2008,13(3).
[34]Christos Tzomakas,Werner von Seelen.Vehicle Detection in Traffic Scenes Using Shadows.Technical Report 98-06,Institut für Neuroinformatik Ruhr-Universitat Bochum.1998.
[35]Milan Sonka,Vaclav Hlavac,Roger Boyle.图像处理、分析与机器视觉(第二版).人民邮电出版社.2003.
[36]张妙兰 付新文.一种纹理图象分类方法的研究.中国图象图形学报.1999,4(8):680-683.
[37]吴谨,李娟,刘成云,夏贝贝.基于最大熵的灰度阈值选取方法.武汉科技大学学报(自然科学版).2004,27(1):58-60.
[38]庞全,苏佳,段会龙.基于四叉树和交叉熵的面向对象图像分割方法.浙江大学学报(工学版).2004,38(12):1615-1618。
[39]黄春艳,杨国胜,侯艳丽.基于熵的图像二值化方法研究.河南大学学报(自然科学版).2005,35(2):76-78.
[40]吴刚,杨敬安,李道伦,张浩.平面图像的对称性检测方法研究.计算机科学.2001,28(5):101-103.
[41]高翔,王勇.数据融合技术综述.计算机测量与控制.2002,10(11):706-709.
[42]李颖丽,彭亦功.数据融合技术及其应用.智能检测控制技术及仪表装置发展研讨会论文集.2007:103-108.
[43]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报.2002,25(3):225-237.
[44]邵文坤,黄爱民,韦庆.目标跟踪方法综述.影像技术.2006(1):17-20.
[45]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.公路交通科技.2007,24(12):127-131.
[46]Kalman R.E..A New Approach to Linear Filtering and Prediction Problems. Transaction of the ASME-Journal of Basic Engineering.1960,82(1):35-45.
[47]袁基炜,史忠科.一种基于灰色预测模型GM(1,1)的运动车辆跟踪方法.控制与决策.2006,21(3):300-304.
[48]万琴,王耀南.基于卡尔曼滤波器的运动目标检测与跟踪.湖南大学学报(自然科学版).2007,34(3):36-40.
[49]杜云峰.卡尔曼滤波器在过程估计中的应用.机电产品开发与创新.2007,20(4):142-143.
[50]吴园.运动车辆的检测与跟踪.南京航空航天大学硕士学位论文.2007.
[51]冯仰敏.运动目标图像检测与跟踪问题研究.西安建筑科技大学硕士学位论文.2007.
[2]http://www.mps.gov.cn/cenweb/brjlCenweb/jsp/common/article.jsp?infoid=ABC00000000000042969.
[3]袁泉.基于“以人为中心”的汽车安全性研究.汽车安全.1998(6):37-39.
[4]熊和金.智能汽车系统研究的若干问题.交通运输工程学报.2001,1(2):37-40.
[5]Richard Bishop.Intelligent vehicle applications worldwide.IEEE Intelligent Systems.2000,15(1):78-81.
[6]李斌,王荣本,郭克友.基于机器视觉的智能车辆障碍物检测方法研究.公路交通科技.2002,19(4):126-129.
[7]Zehang Sun,George Bebis,Ronald Miller.On-Road Vehicle Detection:A Review.IEEE Transactions on Pattern Analysis and Machine Intelligence.2006,28(5):694-711.
[8]Hideo Mori,N.Moghadam Charkari.Shadow and Rhythm as Sign patterns of Obstacle Detection.IEEE International Symposium on Industrial Electronics.1993:271-277.
[9]T.K.ten Kate,M.B.van Leewen,S.E.Moro-Ellenberger,B.J.F.Driessen,A.H.G.Versluis,F.C.A.Groen.Mid-range and Distant Vehicle Detection with a Mobile Camera.IEEE Intelligent Vehicles Symposium.2004:72-77.
[10]Clady X.,Collange F,Jurie F.,Martinet P..Cars detection and tracking with a vision sensor.IEEE Intelligent Vehicles Symposium.2003:593-598.
[11]Sam Yong Kim,Se-Young Oh,JeongKwan Kang,Young Woo Ryu,Kwangsoo Kim,Sang-Cheol Park,KyongHa Park.Front and Rear Vehicle Detection and Tracking in the Day and Night Times Using Vision and Sonar Sensor Fusion.IEEE/RSJ International Conference on Intelligent Robots and Systems.2005:2173-2178.
[12]Shih-Shinh Huang,Chung-Jen Chen,Pei-Yung Hsiao,Li-Chen Fu.On-Board Vision System for Lane Recognition and Front-Vehicle Detection to Enhance Driver's Awareness.IEEE International Conference on Robotics & Automation.2004:2456-2461.
[13]Tie Liu,Nanning Zheng,Li Zhao,Hong Cheng.Learning based Symmetric Features Selection for Vehicle Detection.IEEE Intelligent Vehicles Symposium.2005:124-129.
[14]Thomas Zielke,Michael Brauckmann,Wemer von Seelen.Intensity and Edge-based Symmetry Detection with an Application to Car-following.Computer Vision,Graphics,and Image Processing:Image Understanding.1993,58(2):177-190.
[15]Chu Jiangwei,Ji Lisheng,Guo Lie,Libibing,Wang Rongben.Study on Method of Detecting Preceding Vehicle Based on Monocular Camera.IEEE Intelligent Vehicles Symposium.2004:750-755.
[16]A.Bensrhair,M.Bertozzi,A.Broggi,P.Mich.A Cooperative Approach to Vision-based Vehicle Detection.IEEE Intelligent Transportation Systems.2001:207-212.
[17]陈慧芳,严惠民,姚晓强.被动测距系统汽车目标提取算法研究.浙江大学学报(工学版).2005,39(4):526-529.
[18]范培蕾,张晓今,廖传锦,寇保华.基于视觉的高速公路目标检测与跟踪系统研究.重庆邮电学院学报(自然科学版).2005,17(6):776-779.
[19]Massimo Bertozzi,Alberto Broggi,Stefano Castelluccio.A real-time oriented system for vehicle detection.Journal of Systems Architecture.1997:317-325.
[20]周福良,严惠民.高速公路汽车测距系统中目标汽车提取的研究.公路交通科技.2003,20(5):87-90.
[21]刘勃,周荷琴,魏铭旭.基于颜色和运动信息的夜间车辆检测方法.中国图象图形学报.2005,10(2):187-191.
[22]Thomas Kalinke,Christos Tzomakas,Werner v.Seelen.A Texture-based Object Detection and an Adaptive Model-Based Classification.IEEE International Conference on Intelligent Vehicles.1998:143-148.
[23]Uwe Handmann and Thomas Kalinke.Fusion of Texture and Contour Based Methods for Object Recognition.IEEE Intelligent Transportation System.1997:876-881.
[24]吴珺文,张学工.应用小波变换和PCA进行车辆的静态图像检测.清华大学学报(自然科学版).2002,42(11):1560-1564.
[25]Zehang Sun,Ronald Miller,George Bebis,David DiMeo.A Real-time Precrash Vehicle Detection System.IEEE Workshop on Applications of Computer Vision.2002:171-176.
[26]Zehang Sun,George Bebis,Ronald Miler.On-Road Vehicle Detection Using Gabor Filters and Support Vector Machines.International Conference on Digital Signal Processing.2002,2:1019-1022.
[27]Berthold K.P.Horn,Brian G.Schunck.Determining Optical Flow.Artificial Intelligence.1981,17(123):185-204.
[28]Parag H.Batavia,Dean A.Pomerleau,Charles E.Thorpe.Overtaking Vehicle Detection Using Implicit Optical Flow.IEEE Conference on Intelligent Transportation System.1997:729-734.
[29]Junxian Wang,George Bebis,Ronald Miller.Overtaking Vehicle Detection Using Dynamic and Quasi-Static Background Modeling.IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2005,3:64-64.
[30]Lefaix G.,Marchand T.,Bouthemy P..Motion-based Obstacle Detection and Tracking for Car Driving Assistance.IEEE International Conference on Pattern Recognition.2002,4:74-77.
[31]ZENG Zhi-Hong.Lane Detection and Car Tracking on the Highway.自动化学报.2003,29(3):450-456.
[32]J.M.Collado,C.Hilario,A.de la Escalera,J.M Armingol.Model Based Vehicle Detection for Intelligent Vehicles.IEEE Intelligent Vehicles Symposium.2004:572-577.
[33]胡铟,杨静宇.基于模型的车辆检测及跟踪.中国图象图形学报.2008,13(3).
[34]Christos Tzomakas,Werner von Seelen.Vehicle Detection in Traffic Scenes Using Shadows.Technical Report 98-06,Institut für Neuroinformatik Ruhr-Universitat Bochum.1998.
[35]Milan Sonka,Vaclav Hlavac,Roger Boyle.图像处理、分析与机器视觉(第二版).人民邮电出版社.2003.
[36]张妙兰 付新文.一种纹理图象分类方法的研究.中国图象图形学报.1999,4(8):680-683.
[37]吴谨,李娟,刘成云,夏贝贝.基于最大熵的灰度阈值选取方法.武汉科技大学学报(自然科学版).2004,27(1):58-60.
[38]庞全,苏佳,段会龙.基于四叉树和交叉熵的面向对象图像分割方法.浙江大学学报(工学版).2004,38(12):1615-1618。
[39]黄春艳,杨国胜,侯艳丽.基于熵的图像二值化方法研究.河南大学学报(自然科学版).2005,35(2):76-78.
[40]吴刚,杨敬安,李道伦,张浩.平面图像的对称性检测方法研究.计算机科学.2001,28(5):101-103.
[41]高翔,王勇.数据融合技术综述.计算机测量与控制.2002,10(11):706-709.
[42]李颖丽,彭亦功.数据融合技术及其应用.智能检测控制技术及仪表装置发展研讨会论文集.2007:103-108.
[43]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报.2002,25(3):225-237.
[44]邵文坤,黄爱民,韦庆.目标跟踪方法综述.影像技术.2006(1):17-20.
[45]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.公路交通科技.2007,24(12):127-131.
[46]Kalman R.E..A New Approach to Linear Filtering and Prediction Problems. Transaction of the ASME-Journal of Basic Engineering.1960,82(1):35-45.
[47]袁基炜,史忠科.一种基于灰色预测模型GM(1,1)的运动车辆跟踪方法.控制与决策.2006,21(3):300-304.
[48]万琴,王耀南.基于卡尔曼滤波器的运动目标检测与跟踪.湖南大学学报(自然科学版).2007,34(3):36-40.
[49]杜云峰.卡尔曼滤波器在过程估计中的应用.机电产品开发与创新.2007,20(4):142-143.
[50]吴园.运动车辆的检测与跟踪.南京航空航天大学硕士学位论文.2007.
[51]冯仰敏.运动目标图像检测与跟踪问题研究.西安建筑科技大学硕士学位论文.2007.