车辆视频检测与跟踪系统的研究与实现
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摘要
车辆视频检测与跟踪系统的主要目标是在不破坏路面的情况下,获取道路车流量、车长、车速、占道率等交通信息,为道路的宏观管理和公路规划设计提供重要科学依据。同时, 动态场景中运动物体的快速分割、光线变化、多车辆粘连、车辆遮挡的处理等问题也给车辆视频检测与跟踪系统的研究带来了一定的挑战。
为了解决这些问题,本文提出了静止摄像机条件下的分别基于线式车辆视频检测器与基于面式车辆视频检测器的运动目标检测与跟踪算法。
本文分五个部分进行介绍:
(1)简单介绍了车辆视频检测系统的研究背景及实现车辆视频检测与跟踪系统的各种方法。并介绍了目前普遍采用的车辆视频检测器。
(2)第二章提出的车辆检测系统采用线式车辆视频检测器和背景消减算法进行运动车辆的检测,通过用户设置虚拟线圈划定检测区域。为了使该系统用于十字路口,采用设置背景缓冲区的方式进行背景的提取与更新,不将停止在路口的车辆捕获为背景。给出实验结果,并比较其性能。该系统能较准确完成运动车辆的计数与测速功能。
(3)针对线式车辆检测器的不足,第三章提出的车辆视频检测与跟踪系统采用面式车辆检测器,无须用户设置检测区域,在图像的全部范围内进行车辆的检测与跟踪。采用了高阶统计量与低阶统计量相结合为度量的背景提取算法。自适应背景更新算法则是对每帧图像与背景相比较,从它们的差值进行背景小
步长更新。通过类间方差求得最佳分割阈值,采用竖向膨胀算子解决易出现的车辆断层问题,通过种子填充算法找到连通的车辆区域,完成车辆的检测,给出实验结果。针对第一套系统,该系统取得了更为理想的效果,从实验结果中看到提取了完整与准确的背景,背景更新的效果也非常理想,通过阈值分割与其它的数字图像处理算法,检测的结果大部分是正确的。无法通过上述方法解决车辆遮挡问题,但在一定程度上解决了车辆粘连问题。
(4)给出了简单的摄像机校准方法,并提出了基于Kalman滤波的运动车辆跟踪技术。在“匹配——修正——预测”过程中实现车辆的运动跟踪。为减少计算量,认为两个坐标无关,采用两个Kalman滤波分别进行两个方向的跟踪。从实验结果看到取得比较理想的运动目标跟踪效果。
(5)提出了基于线式检测器与背景消减算法的车辆视频检测系统的实现及设计过程,对每一个模块的功能进行了设计与分析,并给出系统流程。
本系统针对车辆视频检测与跟踪系统的一些问题,提出了一些解决方法,通过实验证明,该方法可以运用于实时环境,背景提取与更新算法具有可行性,检测与跟踪结果也比较理想。
Research & Implementation of Video Vehicle Detection and Tracking system
The video vehicle detection and tracking system is used to get traffic information, such as vehicle flow, vehicle length, vehicle velocity and the roads utilization without destroying the roads. It particularly emphasizes on the management of roads such as the traffic management, road design layout etc. Moreover, there are many problems, such as fast segmentation of moving objects, change of light, vehicle sheltering each other, and vehicle blocking, which make many difficulties to the vehicle video detection and tracking system.
This paper provides two vehicle video detection and tracking algorithms based on line-type vehicle video detector and area-type video vehicle detector.
The paper includes five parts:
The first chapter simply introduces the research background of the system,all kinds of ways to implement the video vehicle detection and tracking system and a few of video vehicle detectors which are used widely today.
The first video vehicle detection system introduced by Chapter 2 uses the line-type vehicle detector, background subtraction algorithm. And the detection area which is set by operators to detect vehicles. In our system the background buffer is used to obtain and update the right background in order to use the system at the crossroads. At last, the real results are provided and the performance is analyzed. The system can count moving vehicles and calculate the vehicle velocity correctly.
According the shortcomings of the line-type vehicle detector, the video vehicle detection and tracking system introduced by chapter 3 uses area-type video detector which uses the whole area as detection area instead of the area set by operators. The background obtainment algorithm uses high order statistics and low order statistics. Adaptive background update algorithm uses small-step-long
value, which is determined by the subtraction of the current and previous picture, to update the background. The best threshold value is gotten through the maximum variance. For the vehicle image edge-broken problem, we use dilation operator. At last, seed filling algorithm finished the vehicle detection. The real results are provided to make a comparison with the first system. The second system can get the whole and accurate background with better performance. When the environment changes, the background updating algorithm is effective. The detected results are almost correct through the series of digital image processes, such as threshold segmentation and seed filling algorithm. But we cannot resolve the vehicle block problem completely.
The fourth chapter describes the simple camera adjusting method. Meanwhile, the vehicle tracking algorithm based on Kalman filter is advanced. The moving tracking is achieved by three steps that are the matching step, emendation step, prediction step. In order to reduce the compute complexity, the X and Y coordinates are assumed that there is no relationship between them. So the X and Y directions can be tracked separately using the Kalman filters. The results are proved the efficiency of the method.
At last, the design and implementation of the video vehicle detection system based on line-type video detector are introduced.
The paper provides many ways to resolve the difficult problems of video vehicle detection and tracking system. The system can be used in real-time environment. Moreover, the background obtainment and renewal algorithms are viable and results are perfect.
为了解决这些问题,本文提出了静止摄像机条件下的分别基于线式车辆视频检测器与基于面式车辆视频检测器的运动目标检测与跟踪算法。
本文分五个部分进行介绍:
(1)简单介绍了车辆视频检测系统的研究背景及实现车辆视频检测与跟踪系统的各种方法。并介绍了目前普遍采用的车辆视频检测器。
(2)第二章提出的车辆检测系统采用线式车辆视频检测器和背景消减算法进行运动车辆的检测,通过用户设置虚拟线圈划定检测区域。为了使该系统用于十字路口,采用设置背景缓冲区的方式进行背景的提取与更新,不将停止在路口的车辆捕获为背景。给出实验结果,并比较其性能。该系统能较准确完成运动车辆的计数与测速功能。
(3)针对线式车辆检测器的不足,第三章提出的车辆视频检测与跟踪系统采用面式车辆检测器,无须用户设置检测区域,在图像的全部范围内进行车辆的检测与跟踪。采用了高阶统计量与低阶统计量相结合为度量的背景提取算法。自适应背景更新算法则是对每帧图像与背景相比较,从它们的差值进行背景小
步长更新。通过类间方差求得最佳分割阈值,采用竖向膨胀算子解决易出现的车辆断层问题,通过种子填充算法找到连通的车辆区域,完成车辆的检测,给出实验结果。针对第一套系统,该系统取得了更为理想的效果,从实验结果中看到提取了完整与准确的背景,背景更新的效果也非常理想,通过阈值分割与其它的数字图像处理算法,检测的结果大部分是正确的。无法通过上述方法解决车辆遮挡问题,但在一定程度上解决了车辆粘连问题。
(4)给出了简单的摄像机校准方法,并提出了基于Kalman滤波的运动车辆跟踪技术。在“匹配——修正——预测”过程中实现车辆的运动跟踪。为减少计算量,认为两个坐标无关,采用两个Kalman滤波分别进行两个方向的跟踪。从实验结果看到取得比较理想的运动目标跟踪效果。
(5)提出了基于线式检测器与背景消减算法的车辆视频检测系统的实现及设计过程,对每一个模块的功能进行了设计与分析,并给出系统流程。
本系统针对车辆视频检测与跟踪系统的一些问题,提出了一些解决方法,通过实验证明,该方法可以运用于实时环境,背景提取与更新算法具有可行性,检测与跟踪结果也比较理想。
Research & Implementation of Video Vehicle Detection and Tracking system
The video vehicle detection and tracking system is used to get traffic information, such as vehicle flow, vehicle length, vehicle velocity and the roads utilization without destroying the roads. It particularly emphasizes on the management of roads such as the traffic management, road design layout etc. Moreover, there are many problems, such as fast segmentation of moving objects, change of light, vehicle sheltering each other, and vehicle blocking, which make many difficulties to the vehicle video detection and tracking system.
This paper provides two vehicle video detection and tracking algorithms based on line-type vehicle video detector and area-type video vehicle detector.
The paper includes five parts:
The first chapter simply introduces the research background of the system,all kinds of ways to implement the video vehicle detection and tracking system and a few of video vehicle detectors which are used widely today.
The first video vehicle detection system introduced by Chapter 2 uses the line-type vehicle detector, background subtraction algorithm. And the detection area which is set by operators to detect vehicles. In our system the background buffer is used to obtain and update the right background in order to use the system at the crossroads. At last, the real results are provided and the performance is analyzed. The system can count moving vehicles and calculate the vehicle velocity correctly.
According the shortcomings of the line-type vehicle detector, the video vehicle detection and tracking system introduced by chapter 3 uses area-type video detector which uses the whole area as detection area instead of the area set by operators. The background obtainment algorithm uses high order statistics and low order statistics. Adaptive background update algorithm uses small-step-long
value, which is determined by the subtraction of the current and previous picture, to update the background. The best threshold value is gotten through the maximum variance. For the vehicle image edge-broken problem, we use dilation operator. At last, seed filling algorithm finished the vehicle detection. The real results are provided to make a comparison with the first system. The second system can get the whole and accurate background with better performance. When the environment changes, the background updating algorithm is effective. The detected results are almost correct through the series of digital image processes, such as threshold segmentation and seed filling algorithm. But we cannot resolve the vehicle block problem completely.
The fourth chapter describes the simple camera adjusting method. Meanwhile, the vehicle tracking algorithm based on Kalman filter is advanced. The moving tracking is achieved by three steps that are the matching step, emendation step, prediction step. In order to reduce the compute complexity, the X and Y coordinates are assumed that there is no relationship between them. So the X and Y directions can be tracked separately using the Kalman filters. The results are proved the efficiency of the method.
At last, the design and implementation of the video vehicle detection system based on line-type video detector are introduced.
The paper provides many ways to resolve the difficult problems of video vehicle detection and tracking system. The system can be used in real-time environment. Moreover, the background obtainment and renewal algorithms are viable and results are perfect.
引文
[1] 基于二维时空图像分析的车辆检测方法研究,胥健,2001.1,万方学位论文数据库,http://202.115.61.27:83/
[2] 交通监控中视觉信息的检测与跟踪技术的研究,王春波,2000.6,万方学位论文数据库,http://202.115.61.27:83/
[3] 基于机器视觉技术的城市交通预警系统的研究 ,朱茵 唐祯敏 朱 钧 ,http://www.sinosurveillance.com/achievement/zhuyin.pdf
[4] Haritaoglu I, Harwood D and Davis L. W: real-time surveillance of people and their activities. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (8): 809-830.
[5] McKenna S et al, Tracking groups of people. Computer Vision and Image Understanding, 2000, 80 (1): 42-56.
[6] Karmann K and Brandt A. Moving object recognition using an adaptive background memory. In: V Cappellini, Time-varying Image Processing and Moving Object Recognition. 2. Elsevier, Amsterdam, The Netherlands, 1990.
[7] Kilger M. A shadow handler in a video-based real-time traffic monitoring system. In: Proc IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, 1992, 1060-1066.
[8] Stauffer C and Grimson W. Adaptive background mixture models for real-time tracking. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 1999, 2: 246-252.
[9] Wren C, Azarbayejani A, Darrell T and Pentland A. Pfinder: real-time tracking of the human body. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19 (7): 780-785.
[10] 人运动的视觉分析综述,王 亮,胡卫明,谭铁牛,《计算机学报》 25 卷,第 3 期,2002
[11] Lipton A, Fujiyoshi H and Patil R. Moving target classification and tracking from real-time video. In: Proc IEEE Workshop on Applications of Computer Vision, Princeton, NJ, 1998, 8-14.
[12] Anderson C, Bert P and Vander Wal G. Change detection and tracking using pyramids transformation techniques. In: Proc SPIE Conference on Intelligent Robots and Computer Vision,
Cambridge, MA, 1985, 579: 72-78.
[13] Barron J, Fleet D and Beauchemin S. Performance of optical flow techniques. International Journal of Computer Vision, 1994, 12 (1): 42-77.
[14] Ju S, Black M and Yaccob Y. Cardboard people: a parameterized model of articulated image motion. In: Proc IEEE International Conference on Automatic Face and gesture Recognition, Killington, Vermont USA, 1996, 38-44.
[15] Stauffer C and Grimson W. Adaptive background mixture models for real-time tracking. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 1999, 2: 246-252.
[16] McKenna S et al, Tracking groups of people. Computer Vision and Image Understanding, 2000, 80 (1): 42-56.
[17] Paragios N and Deriche R. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (3): 266-280.
[18] Bertalmio M, Sapiroo G and Randll G. Morphing active contours. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (7): 733-737.
[19] Peterfreund N. Robust tracking of position and velocity with Kalman snakes. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (6): 564-569.
[20] Isard M and Blake A. Contour tracking by stochastic propagation of conditional density. In: Proc European Conference on Computer Vision, Cambridge, 1996, 343-356.
[21] Baumberg A and Hogg D. An efficient method for contour tracking using active shape models. In: Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, 1994, 194-199.
[22] 83 Polana R and Nelson R. Low level recognition of human motion. In: Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, TX, 1994, 77-82.
[23] Segen J and Pingali S. A camera-based system for tracking people in real time. In: Proc International Conference on Pattern recognition, Vienna, 1996, 63-67.
[24] Utsumi A, Mori H, Ohya J and Yachida M. Multiple-view-based tracking of multiple humans. In: Proc IEEE International Conference on Pattern Recognition, Brisbane, Australia, 1998, 597-601.
[25] Karaulova I, Hall P and Marshall A. A hierarchical model of dynamics for tracking people with a single video camera. In: British Machine Vision Conference, Bristol, UK, 2000, 352-361.
[26] Niyogi S and Adelson E. Analyzing and recognizing walking figures in XYT. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Seattle, USA, 1994, 469-474.
[27] Wachter S and Nagel H-H. Tracking persons in monocular image sequences. Computer Vision and Image Understanding, 1999, 74 (3): 174-192.
[28] 智能交通系统中的一些算法的研究与实现,王荣,《四川大学硕士学位论文》,2001,8-9。
[30] N. H. C. Yung, K. C. Chan, and A. H. S. Lai, “Vehicle-type identification through automated virtual loop assignment and block-based motion estimation,” in Proc. IEEE/IEEJ/JSAI Int. Conf. Intelligent Transportation Systems, 1999, pp. 692–696.
[31] A. H. S. Lai, “An effective methodology for visual traffic surveillance,” Ph.D. dissertation, Univ. Hong Kong, Jan. 2000.
[32] 《时间序列分析—高阶统计量方法》,张贤达著,1995,p56~100.
[33] 《车辆检测模型研究》(重庆大学博士论文),王刚, 2001,p50~60。
[34] Young-Kee Jung, Kyu-Won Lee, and Yo-Sung Ho, Content-Based Event Retrieval Using Semantic Scene Interpretation for Automated Traffic Surveillance , IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 2, NO. 3, SEPTEMBER 2001 151
[35] Daniel J. Dailey, Member, IEEE, F. W. Cathey, and Suree Pumrin, An Algorithm to Estimate Mean Traffic Speed UsingUncalibrated Cameras, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 1, NO. 2, JUNE 2000
[36]Kenneth R. Castleman, 朱志刚,林学訚,石定机译,数字图像处理,p378
[37] W.H.Press, S.A.Teukolsky, W.T.Vettering, and B.P. Flannery, Numerical Recipes in C(2d ed), Cambridege University Press, 1992.
[38]何斌,马天予,王运坚,朱红莲,Visual C++数字图像处理,人民邮电出版社,p335~400,2001.
[39] Daniel J. Dailey, F. W. Cathey, and Suree Pumrin,An Algorithm to Estimate Mean Traffic Speed Using Uncalibrated Cameras, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 1, NO. 2, JUNE 2000
[40] Amer. Assoc. State Highway and Transportation Officials, , “A policy on geometric design of highways and streets, 1994,” Amer. Assoc. State Highway and Transportation Officials, Washington, DC, 1994.
[41] 王树桦,软件工程的若干探讨,系统分析员之窗,Email: jnx@yeah.net,2001,8,摘录。
[42] 蒋欣荣,图像分析和头影测量预测系统的设计与实现,四川大学硕士学位论文,1999。
[2] 交通监控中视觉信息的检测与跟踪技术的研究,王春波,2000.6,万方学位论文数据库,http://202.115.61.27:83/
[3] 基于机器视觉技术的城市交通预警系统的研究 ,朱茵 唐祯敏 朱 钧 ,http://www.sinosurveillance.com/achievement/zhuyin.pdf
[4] Haritaoglu I, Harwood D and Davis L. W: real-time surveillance of people and their activities. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (8): 809-830.
[5] McKenna S et al, Tracking groups of people. Computer Vision and Image Understanding, 2000, 80 (1): 42-56.
[6] Karmann K and Brandt A. Moving object recognition using an adaptive background memory. In: V Cappellini, Time-varying Image Processing and Moving Object Recognition. 2. Elsevier, Amsterdam, The Netherlands, 1990.
[7] Kilger M. A shadow handler in a video-based real-time traffic monitoring system. In: Proc IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, 1992, 1060-1066.
[8] Stauffer C and Grimson W. Adaptive background mixture models for real-time tracking. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 1999, 2: 246-252.
[9] Wren C, Azarbayejani A, Darrell T and Pentland A. Pfinder: real-time tracking of the human body. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19 (7): 780-785.
[10] 人运动的视觉分析综述,王 亮,胡卫明,谭铁牛,《计算机学报》 25 卷,第 3 期,2002
[11] Lipton A, Fujiyoshi H and Patil R. Moving target classification and tracking from real-time video. In: Proc IEEE Workshop on Applications of Computer Vision, Princeton, NJ, 1998, 8-14.
[12] Anderson C, Bert P and Vander Wal G. Change detection and tracking using pyramids transformation techniques. In: Proc SPIE Conference on Intelligent Robots and Computer Vision,
Cambridge, MA, 1985, 579: 72-78.
[13] Barron J, Fleet D and Beauchemin S. Performance of optical flow techniques. International Journal of Computer Vision, 1994, 12 (1): 42-77.
[14] Ju S, Black M and Yaccob Y. Cardboard people: a parameterized model of articulated image motion. In: Proc IEEE International Conference on Automatic Face and gesture Recognition, Killington, Vermont USA, 1996, 38-44.
[15] Stauffer C and Grimson W. Adaptive background mixture models for real-time tracking. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 1999, 2: 246-252.
[16] McKenna S et al, Tracking groups of people. Computer Vision and Image Understanding, 2000, 80 (1): 42-56.
[17] Paragios N and Deriche R. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (3): 266-280.
[18] Bertalmio M, Sapiroo G and Randll G. Morphing active contours. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (7): 733-737.
[19] Peterfreund N. Robust tracking of position and velocity with Kalman snakes. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22 (6): 564-569.
[20] Isard M and Blake A. Contour tracking by stochastic propagation of conditional density. In: Proc European Conference on Computer Vision, Cambridge, 1996, 343-356.
[21] Baumberg A and Hogg D. An efficient method for contour tracking using active shape models. In: Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, 1994, 194-199.
[22] 83 Polana R and Nelson R. Low level recognition of human motion. In: Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, TX, 1994, 77-82.
[23] Segen J and Pingali S. A camera-based system for tracking people in real time. In: Proc International Conference on Pattern recognition, Vienna, 1996, 63-67.
[24] Utsumi A, Mori H, Ohya J and Yachida M. Multiple-view-based tracking of multiple humans. In: Proc IEEE International Conference on Pattern Recognition, Brisbane, Australia, 1998, 597-601.
[25] Karaulova I, Hall P and Marshall A. A hierarchical model of dynamics for tracking people with a single video camera. In: British Machine Vision Conference, Bristol, UK, 2000, 352-361.
[26] Niyogi S and Adelson E. Analyzing and recognizing walking figures in XYT. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Seattle, USA, 1994, 469-474.
[27] Wachter S and Nagel H-H. Tracking persons in monocular image sequences. Computer Vision and Image Understanding, 1999, 74 (3): 174-192.
[28] 智能交通系统中的一些算法的研究与实现,王荣,《四川大学硕士学位论文》,2001,8-9。
[30] N. H. C. Yung, K. C. Chan, and A. H. S. Lai, “Vehicle-type identification through automated virtual loop assignment and block-based motion estimation,” in Proc. IEEE/IEEJ/JSAI Int. Conf. Intelligent Transportation Systems, 1999, pp. 692–696.
[31] A. H. S. Lai, “An effective methodology for visual traffic surveillance,” Ph.D. dissertation, Univ. Hong Kong, Jan. 2000.
[32] 《时间序列分析—高阶统计量方法》,张贤达著,1995,p56~100.
[33] 《车辆检测模型研究》(重庆大学博士论文),王刚, 2001,p50~60。
[34] Young-Kee Jung, Kyu-Won Lee, and Yo-Sung Ho, Content-Based Event Retrieval Using Semantic Scene Interpretation for Automated Traffic Surveillance , IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 2, NO. 3, SEPTEMBER 2001 151
[35] Daniel J. Dailey, Member, IEEE, F. W. Cathey, and Suree Pumrin, An Algorithm to Estimate Mean Traffic Speed UsingUncalibrated Cameras, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 1, NO. 2, JUNE 2000
[36]Kenneth R. Castleman, 朱志刚,林学訚,石定机译,数字图像处理,p378
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