基于视觉的交通违章停车检测方法研究
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摘要
随着我国城市化进程的不断加快,城市公路交通系统的压力不断加大,交通违章停车这一交通问题日益突显出来,对交通正常秩序的维护造成了非常恶劣的影响。本文基于视觉技术对违章停车的检测方法进行了研究,同时设计和实现了车辆违章停车自动抓拍系统。在对系统进行了现场测试的基础上对系统的性能进行了详细的分析。论文的主要内容如下:
首先,论文对监控场景中运动车辆动态检测问题进行了研究。在分析比较目前比较常用的几种检测方法的基础上,应用了一种基于时间的运动历史图像的方法。这种方法可以充分利用监控视频携带的信息,对运动目标的表达独立于处理系统的性能,有较强的实时性。同时,论文在这一部分利用了一种基于阴影特性的阴影消除方法对与车辆一同被检出的车辆阴影进行了消除,实现了对运动车辆的准确检测。
其次,论文对运动车辆的跟踪方法进行了研究。论文详细论述了跟踪匹配原理,并比较分析了几种常用跟踪方法。针对论题的应用,本文应用了特征点光流估计匹配的跟踪方法。这种方法实时性强,对车辆粘连、遮挡问题有一定的鲁棒性,并且应用这种方法很容易对车辆的违章行为进行判断,实现了在监控场景下的有效跟踪。
再次,论文设计了高速球型摄像机精确定位算法。本文结合了线性摄像机成像模型及高速球型摄像机通信协议,对跟踪的违章车辆目标实现了二次定位,最终利用车牌识别软件可以完成对车牌的识别。并且在定位过程中可以获取了车辆违章的证据,从而完成了对违章车辆的处理。
最后,在前述算法基础上本文设计了车辆违章停车自动抓拍系统。本文对系统的总体构架、各模块的设计、性能指标的评估方法进行了论述。并且论文还在不同的天气状况下,在实际场景中对该系统进行了实验,并从系统准确性、实时性、稳定性等方面对系统进行了评估。实验结果表明,该系统是车辆违章停车问题一种切实有效的解决方案。
With the development of city and economy, the burden of the road transportation system becomes heavier and heavier. Illegal parking has become a more serious problem which has a very bad affection on normal transportation order. The paper is devoted to detecting method of illegal parking based on vision technology and illegal parking automatically snapshot system is designed and realized. What is more the paper analyzes the performance of the system in detail on the basis of field testing. The main contents of the paper are listed as follows:
Firstly, this paper focuses on moving vehicle detection problem in dynamic scenes. A method named time-based motion history image is applied after analyzing and comparing several normal used methods. This method sufficiently uses the information surveillance video carries and the moving object is depicted independent of processing system. As a result this method has good real-time performance. Meanwhile this paper applies method based on shadow character to remove vehicle shadow which makes the moving detection more precise.
Secondly, this paper researches on moving vehicle tracking method. Matching theory and several normal used methods are introduced. According to this specific application in this paper characteristic points optical-flow estimation matching tracking method is used. It has good real-time performance and is robust for vehicle adhesion and shading. Furthermore this method makes it convenient to judge vehicle behavior which realizes effective tracking in surveillance scene.
Thirdly, this paper designs accurate location algorithm based on high speed dome camera. This algorithm combines linear camera imaging model and communication protocol and locates peccancy vehicle twice. Then the vehicle plate can be recognized by using license plate recognition software. In this process peccancy evidence image can also be achieved, then the processing to peccancy vehicle is over.
Finally, on the basis of all these algorithms above, automatic snapshot system for illegal parking is designed. The frame of the system design of each module and estimation method of performance index are described specifically. Under different kinds of weather circumstance the system is tested in real scene. Then, the system performance including accurate capability real-time capability and stability is estimated. The result shows that this system is an effective solution for illegal parking problem.
首先,论文对监控场景中运动车辆动态检测问题进行了研究。在分析比较目前比较常用的几种检测方法的基础上,应用了一种基于时间的运动历史图像的方法。这种方法可以充分利用监控视频携带的信息,对运动目标的表达独立于处理系统的性能,有较强的实时性。同时,论文在这一部分利用了一种基于阴影特性的阴影消除方法对与车辆一同被检出的车辆阴影进行了消除,实现了对运动车辆的准确检测。
其次,论文对运动车辆的跟踪方法进行了研究。论文详细论述了跟踪匹配原理,并比较分析了几种常用跟踪方法。针对论题的应用,本文应用了特征点光流估计匹配的跟踪方法。这种方法实时性强,对车辆粘连、遮挡问题有一定的鲁棒性,并且应用这种方法很容易对车辆的违章行为进行判断,实现了在监控场景下的有效跟踪。
再次,论文设计了高速球型摄像机精确定位算法。本文结合了线性摄像机成像模型及高速球型摄像机通信协议,对跟踪的违章车辆目标实现了二次定位,最终利用车牌识别软件可以完成对车牌的识别。并且在定位过程中可以获取了车辆违章的证据,从而完成了对违章车辆的处理。
最后,在前述算法基础上本文设计了车辆违章停车自动抓拍系统。本文对系统的总体构架、各模块的设计、性能指标的评估方法进行了论述。并且论文还在不同的天气状况下,在实际场景中对该系统进行了实验,并从系统准确性、实时性、稳定性等方面对系统进行了评估。实验结果表明,该系统是车辆违章停车问题一种切实有效的解决方案。
With the development of city and economy, the burden of the road transportation system becomes heavier and heavier. Illegal parking has become a more serious problem which has a very bad affection on normal transportation order. The paper is devoted to detecting method of illegal parking based on vision technology and illegal parking automatically snapshot system is designed and realized. What is more the paper analyzes the performance of the system in detail on the basis of field testing. The main contents of the paper are listed as follows:
Firstly, this paper focuses on moving vehicle detection problem in dynamic scenes. A method named time-based motion history image is applied after analyzing and comparing several normal used methods. This method sufficiently uses the information surveillance video carries and the moving object is depicted independent of processing system. As a result this method has good real-time performance. Meanwhile this paper applies method based on shadow character to remove vehicle shadow which makes the moving detection more precise.
Secondly, this paper researches on moving vehicle tracking method. Matching theory and several normal used methods are introduced. According to this specific application in this paper characteristic points optical-flow estimation matching tracking method is used. It has good real-time performance and is robust for vehicle adhesion and shading. Furthermore this method makes it convenient to judge vehicle behavior which realizes effective tracking in surveillance scene.
Thirdly, this paper designs accurate location algorithm based on high speed dome camera. This algorithm combines linear camera imaging model and communication protocol and locates peccancy vehicle twice. Then the vehicle plate can be recognized by using license plate recognition software. In this process peccancy evidence image can also be achieved, then the processing to peccancy vehicle is over.
Finally, on the basis of all these algorithms above, automatic snapshot system for illegal parking is designed. The frame of the system design of each module and estimation method of performance index are described specifically. Under different kinds of weather circumstance the system is tested in real scene. Then, the system performance including accurate capability real-time capability and stability is estimated. The result shows that this system is an effective solution for illegal parking problem.
引文
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2 Hall D L, Llamas J. An Introduction to Multisensor Data Fusion. Proceedings of the IEEE, 1997, 85(1):6-20
3 Bertozzi M, Broggi A. Vision-based Vehicle Guidance, IEEE computer, 1997, 30(7):227-234
4史其信,陆化普.智能交通系统的关键技术及研究发展策略,中国土木工程学会第八届年会论文集,清华大学出版社,1998:70-98
5郁梅,蒋刚毅,郁伯康.智能交通的计算机视觉技术应用,计算机工程与应用,2001,10(2):59-63
6 K. Sage, S. Young. Computer vision for security applications. 32nd Annual 1998 International Carnahan Conference on Security Technology of Proceedings, 1998: 210-215
7 Goujou, E. Miteran, J. Laligant, etc.. A low cost and intelligent video surveillance system, Proceedings of the IEEE International Symposium on Industrial Electronics, 1995, 1(1):405-409
8 Robert T. Collins, Alan J. Lipton. Introduction to the special section on video surveillance, IEEE Transactions on Pattern Analysis and Machine Intelligence, Aug 2000, 22: 745-746
9 Goujou, E. Miteran, J. Laligant, etc.. Human detection with video surveillance system, Proceedings of the IEEE IECON 21st international Conference, 1995,2: 1179-1184
10 Isaac Cohen. Detecting and Tracking Moving Objects for Video Surveillance, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 2: 319-325
11 Zhigang Zhu, Guangyou Xu, Bo Yang, Dingji Shi. VISATERAM: a real-time vision system for automatic traffic monitoring. Image and Vision Computing, 2000, 18: 781-794
12 Suresh Krishnamurthy, Debargha Mukherjee. Video monitoring system framework for video on demand, IEEE Transactions on Consumer Electronics,May 1995, 41(2): 350-359
13 J. L. Ryan. Home automation, Electronics & Communication Engineering Journal, July-Aug 1989, 1(4): 185-192
14 I. Harrison, D. Lupton. Automatic road traffic event monitoring information system, IEEE Seminar on CCTV and Road Surveillance, 1999:61-64
15 B. A. Boghossian, S. A. Velastin. Real-time motion detection of crowds in video signals, IEE Colloquium on High Performance Architectures for Real-Time Image Processing, 1998,124-126
16 Shunsuke Kamijo, Yasuyuki Matsushita. Traffic monitoring and accident detection at intersections, Proceedings of 1999 IEEE, International Conference on Intelligent Transportation Systems, 1999:703-708
17刘大海,卢朝阳.视频技术在智能交通系统中的应用,计算机工程,2003,29(17):165-167
18杨俊,张玲霞,陈明.基于视觉检测的城市智能交通管理系统应用研究,测控技术,2003, 22(2):53-58
19 Ismail Haritaoglu, David Harwood, Larry S. Davis. A real time system for detecting and tracking people, Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998: 222-227
20 Elena Stringa, Carlo S. Regazzoni. Real-time video-shot detection for scene surveillance applications, IEEE transactions on Image Processing, 2000, 9(1): 69-79
21任明,朱伟,朱寿建.视频智能交通系统的设计与实现,交通运输系统工程与信息,2002, 2(3): 79-81
22李智勇等.动态图像分析.国防工业出版社, 1999:101-120
23章毓晋.图像分割.科学出版社,2001:25-60
24 Kameda Yoshinari, Minoh Michihiko. A human motion estimation method using 3-successsive video frames, Proceedings of International. Conference on Virtual Systems and Multimedia, 1996:135-140
25马颂德,张正友.计算机视觉.计算理论和算法基础.科学出版社,1998:36-60
26贾云德.机器视觉.科学出版社,2000:56-110
27程存学,朱晓昆.计算机视觉-底层处理技术,电子工业出版社,1993:20-78
28 Wren C, Azarbayejani A, Darrell T, etc.. Real-time Tracking of the Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7):780-785
29 Jong Bae Kim, Hye Sun Park. A Real-time Region-based Motion Segmentation Using Adaptive Thresholding and K-means Clustering. Springer Berlin:213-224
30 N Paragios, R.Deriche. Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(3):266-280
31王新余,张桂林.基于光流的运动目标实时检测方法研究,计算机工程与应用,2004:23-35
32 W. Enkelmann. Investigation of Multigrid Algorithms for the Estimation of Optical Flow Fields in Image Sequences. Computer Vision, Graphics and Image Processing, 1998, 43:150-170
33 D. Gutchess. A background model initialization algorithm for video surveillance, Proceedings Eighth IEEE International Conference on Computer Vision, July 2001
34周洪仁,王敬东,王德培.机动目标跟踪.国防工业出版社,1991:124-254
35 Chen Baisheng, Lei Yunqi. Indoor and outdoor people detection and shadow suppression by exploiting HSV color information. Proceedings of the Fourth International Conference on Computer and Information Technology
36 Gary R. Bradski, James Davis. Motion segmentation and pose recognition with motion history gradients. Graphics and Image Processing, 2001, 43:115-170
37 Lipton, A. J. Fujiyoshi, H. Patil. Moving target classification and tracking from real-time video. Proc IEEE Workshop on Applications of Computer Vison, Princeton, NF, 1998:8-14
38张丽.车辆视频检测与跟踪系统的算法研究.浙江大学硕士毕业论文,2003.3
39 G.. L. Foresti. Object recognition and tracking for remote video surveillance, IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(7): 1045-1062
40 Gian Luca Forest. Object recognition and tracking for remote video surveillance of unattended outdoor environments, IEEE Transactions on Circuits and System for Video Technology, Oct, 1998, 8(6): 697-704
41 L. Wixson, M. D. Hansen. Detecting salient motion by accumulation directionally- consistent flow, The Proceedings of the Seventh IEEEInternational Conference on Computer Vision in 1992,2:797-804
42 A. Bakowski, G. A. Jones. Video Surveillance tracking using color region adjacency graphs. Seventh International Conference on Image Processing and ITS Applications, 1999, 2:794-798
43 Alan J. Lipton. Local application of optic flow to analyze rigid versus non-rigid motion, ICCV 99 workshop on Frame-Rate Applications, 1999:251-241
44 G.L.Foresti, P.Matteuci, ect. A real-time approach to 3D object tracking in complex scenes, Electron. Lett, 1994, 309(6): 475-477
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