交通视频检测系统中目标检测和跟踪方法研究
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摘要
随着交通的发展,交通视频信息的处理越来越重要。基于视频图像的运动目标检测技术是处理交通视频信息的一个重要手段。如何迅速、准确的检测到交通目标,采集交通信息,是视频检测技术研究的重点。本文在总结和分析现有运动目标检测与跟踪方法的基础上,重点研究静态背景下运动目标的检测和跟踪技术。对静态背景下的背景模型建立与更新、目标分割以及目标跟踪等目标检测关键技术进行了深入研究和探讨,对相关算法进行了改进和实现,对实验结果进行了分析。最后,在研究成果的基础上实现了一个交通检测系统。本论文的工作主要体现在以下几方面：
1、分析了目前流行的各种基于静态背景的运动目标检测原理与算法,总结了各算法的特点,并根据这些方法的特点提出了一种将帧间差分法与背景差分法相结合的改进目标检测算法。
2、在分析比较现有背景提取方法的基础上,提出了一种使用自适应的背景作差法来自动生成背景模型的改进的背景提取方法,并给出了一种带权值因子的背景更新算法。
3、着重讨论了目标分割阈值的选取方法。给出了一种利用最大类间方差法自适应计算分割阈值的方法,并对用该方法进行目标像素点与背景像素点分割的原理和方法进行了详细描述。详细描述了用膨胀、腐蚀处理消除噪声,用区域填充法合并邻域最终获取理想分割目标区域的过程。
4、着重分析了针对颜色特征的MeanShift无参估计目标跟踪算法的原理,并对该算法进行了实现,对实验结果和算法的优劣进行了分析和总结。详细研究了扩展卡尔曼滤波目标跟踪算法,分析了算法的特点,实现了目标的跟踪与外推预测。
5、给出了一种基于团块的跟踪方法。详细描述了基于OpenCV架构的团块目标跟踪方法思想和框架,基于该框架,自定义并实现了一个前景检测模块。
6、使用C++语言结合OpenCV技术,在WindowXP环境的Visualstudio6.0平台上开发了基于视频图像序列的交通视频检测系统。开发了交通参数分析显示子系统。通过系统测试,算法满足可靠性和实时性要求。
With the development of communication, transportation video information processing is becoming more and more important. And video-based motion detection technology is one of the most important methods for transportation video information processing. How to detect the objects to collect traffic information quickly and accurately is the key point of video detection technology study. Based on summarizing and analyzing of current target detection and tracking method, this paper is focusing on moving object detection and tracking technology under static background. Some key technologies on object detection, such as modeling and updating under static background, object segmentation, object tracking are intensive studied and discussed, some of the related algorithms are improved and implemented. And the results of experiments are analyzed. On the end, a transportation detection system is implemented based on these research results. The following work has been down in the paper:
1、After Analyzing popular theories and algorithms of static background-based moving object detection, this paper summarizes all algorithms' features. Based on these features, the paper presents an improved object detection method which combines frame difference and background difference;
2、With Deeply studying on background extraction and analysis of current background extraction, the paper presentes a new background extraction method that is improved from auto-modeling using adaptive background subtraction; Then a background updating algorithm with weight factor is presented after analyzing the theory of background updating;
3、This paper studies basic theory and method of object segmentation; focusing on threshold selection method of object segmentation, the paper describes threshold value calculation method using most Otsu method adaption calculation in details, and then describes theory and method of separating target pixels and background pixels by this method. Particularly, the paper describes the process of eliminating noise by expansion, corrosion method, and combining neighborhood using region filling to obtain the ideal split target area.
4、Based on analyzing the theory of MeanShift target algorithm for color features with-out parameter estimation, the paper implements the algorithm, compare and conclude the advantage and disadvantage of the experiments results and algorithm. Then the paper studies extended Kalman filter tracking algorithm and analyzes its feature. The target tracking and extrapolation is implemented.
5、This paper presents a tracking method based on blob and describes the thought and frame of blob tracking method based on OpenVC architecture. Base on that frame, a detection model is defined and implemented in the paper;
6、This paper uses C++ and OpenVC technology to develop a transportation detection system based on video images sequences on Windows XP with the help of Visual studio 6.0. Then the paper develops a transportation parameters analyzing and displaying subsystem. The system has been passed the tests, the results of tests indicates that algorithm meets reliability and real-time requirements.
1、分析了目前流行的各种基于静态背景的运动目标检测原理与算法,总结了各算法的特点,并根据这些方法的特点提出了一种将帧间差分法与背景差分法相结合的改进目标检测算法。
2、在分析比较现有背景提取方法的基础上,提出了一种使用自适应的背景作差法来自动生成背景模型的改进的背景提取方法,并给出了一种带权值因子的背景更新算法。
3、着重讨论了目标分割阈值的选取方法。给出了一种利用最大类间方差法自适应计算分割阈值的方法,并对用该方法进行目标像素点与背景像素点分割的原理和方法进行了详细描述。详细描述了用膨胀、腐蚀处理消除噪声,用区域填充法合并邻域最终获取理想分割目标区域的过程。
4、着重分析了针对颜色特征的MeanShift无参估计目标跟踪算法的原理,并对该算法进行了实现,对实验结果和算法的优劣进行了分析和总结。详细研究了扩展卡尔曼滤波目标跟踪算法,分析了算法的特点,实现了目标的跟踪与外推预测。
5、给出了一种基于团块的跟踪方法。详细描述了基于OpenCV架构的团块目标跟踪方法思想和框架,基于该框架,自定义并实现了一个前景检测模块。
6、使用C++语言结合OpenCV技术,在WindowXP环境的Visualstudio6.0平台上开发了基于视频图像序列的交通视频检测系统。开发了交通参数分析显示子系统。通过系统测试,算法满足可靠性和实时性要求。
With the development of communication, transportation video information processing is becoming more and more important. And video-based motion detection technology is one of the most important methods for transportation video information processing. How to detect the objects to collect traffic information quickly and accurately is the key point of video detection technology study. Based on summarizing and analyzing of current target detection and tracking method, this paper is focusing on moving object detection and tracking technology under static background. Some key technologies on object detection, such as modeling and updating under static background, object segmentation, object tracking are intensive studied and discussed, some of the related algorithms are improved and implemented. And the results of experiments are analyzed. On the end, a transportation detection system is implemented based on these research results. The following work has been down in the paper:
1、After Analyzing popular theories and algorithms of static background-based moving object detection, this paper summarizes all algorithms' features. Based on these features, the paper presents an improved object detection method which combines frame difference and background difference;
2、With Deeply studying on background extraction and analysis of current background extraction, the paper presentes a new background extraction method that is improved from auto-modeling using adaptive background subtraction; Then a background updating algorithm with weight factor is presented after analyzing the theory of background updating;
3、This paper studies basic theory and method of object segmentation; focusing on threshold selection method of object segmentation, the paper describes threshold value calculation method using most Otsu method adaption calculation in details, and then describes theory and method of separating target pixels and background pixels by this method. Particularly, the paper describes the process of eliminating noise by expansion, corrosion method, and combining neighborhood using region filling to obtain the ideal split target area.
4、Based on analyzing the theory of MeanShift target algorithm for color features with-out parameter estimation, the paper implements the algorithm, compare and conclude the advantage and disadvantage of the experiments results and algorithm. Then the paper studies extended Kalman filter tracking algorithm and analyzes its feature. The target tracking and extrapolation is implemented.
5、This paper presents a tracking method based on blob and describes the thought and frame of blob tracking method based on OpenVC architecture. Base on that frame, a detection model is defined and implemented in the paper;
6、This paper uses C++ and OpenVC technology to develop a transportation detection system based on video images sequences on Windows XP with the help of Visual studio 6.0. Then the paper develops a transportation parameters analyzing and displaying subsystem. The system has been passed the tests, the results of tests indicates that algorithm meets reliability and real-time requirements.
引文
[1]高守传,姚领田.Visualc料数字图像处理与工程应用篇[M].北京：中国铁道出版社.2006,1
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[3]Yasushi Mae,Shinya Yamamoto,Yoshiaki Shirai,et.Opticalflow based rea-ltimeobject tracking by active vision System[EB/OL].http://ww.cv.mech.eng.os-akau.ac.jp/research/ track2ing-group/mae/flow/JFCM94/JFCM94.html
[4]Ryuzo Okada,Yoshiaki Shirai,Jun Miura.Object tracking basedon opticalflowand depth[EB/OL].http://www.cv.mech.eng.osakau.ac.jp/research/tracking-group/okada/flow-disp-track2ing/flow-disp-tracking.html
[5]Shunsuke KAMIJO,Katsushi IKEUCHI,Masao SAKAUCHI. Vehicle Tracking in Low-angle and Front-View Images based on Spatio-Temporal Markov Random Field Model.8th WorldCongress 2001 ITS.Sudney,2001
[6]Yasushi Mae,Shinya Yamamoto,Yoshiaki Shirai,et.Opticalflow based rea-ltimeobject tracking by active vision System[EB/OL].http://www.cv.mech.eng.os-akau.ac.jp/research/ track2ing-group/mae/flow/JFCM94/JFCM94.html
[7]PREVEDOUROS P D, Ji Xiaojin; PAOANDEOU K. Video incidentdetection tests in freeway tunnels [EB/OL],http://pubsindex.trb.org/document/view/default.asp?lbid=77 6527,2009-2-25
[8]Michalopoulos PG. Vehicle detection video through image processing:the autoscope system [J].IEEE Trans. Vehicular Technology,1991,40(1):21-29
[9]Image Sensing Systems, Inc. (ISS).Autoscope Products[EB/OL],http://www.auto scope.com/,2009-02-30
[10]曹仁川,刘天宇,董铁军.全视频技术在电子警察系统中的应用[J].中国信息产业，2004，104-105
[11]张泽旭,李金宗,李宁宁.基于光流场分割和Canny边缘提取融合算法的运动目标检测[J].电子学报.2003,31(9)：1299-1302
[12]魏波.点时空约束图像目标跟踪理论与实时实现技术研究[D].西安：电子科技大学,2000.
[13]周正宇,汪亚明,黄文清等.基于遗传算法聚类的车辆跟踪[J].计算机工程与设计.2004,25(7)：1218-1222
[14]潘薇,游志胜,吴鲲等.基于模糊聚类和卡尔曼滤波的运动目标检测[J]计算机应用.2005,25(1)：123-124
[15]杨杨,张田文.一种基于特征光流的运动目标跟踪方法[J].宇航学报.2000,2(2)：8-15
[16]Gary Bradski,Adrian Kaebler.Learning OpenCV[M].2009
[17]朱秀昌,刘峰,胡栋.数字图像处理与图像通信[J].北京邮电大学出版社.
[18]Horn B,Schunck B.Determining optieal flow.Artificial Intelligence,1981,17:185-203
[19]Anderson C, Bert P, Vander Wal G. Change detection and tracking using pyramids transformation techniques[A].In:Proceedings of SPIE Conference on Intelligent Robots and Computer Vision [C] Cambridge, MA, USA,1985,579:72-78.
[20]Lipton A, Fujiyoshi H, Patil R. Moving target classification and tracking from real-time video[A].In:Proceedings of IEEE Workshop on Applications of Computer Vision[C], Princeton,N J,US A,1998:8-14.
[21]衣秀清.静态场景下运动目标检测与跟踪算法研究[D].山东：山东大学硕士学位论文,2007：7-35.
[22]HARITAOGLU I, DAVIS LARRY S etal.A real time system for detecting and tracking people[A].In:Proceedings of IEEE International Conference on Automatic Face and Geture Recognition. Nara, Japan,1998 [C].
[23]徐瑞鑫.复杂背景下实时目标跟踪与识别研究[D].长春：中国科学院研究生院硕士学位论文,2002：7-14.
[24]N.Otsu.A Threshold Selection Method from Gray-Level Histograms[J].IEEE Trans-actions on Systems,Man and Cybernetics,1979,9(1):62-66.
[25]崔屹.图像处理与分析—数学形态学方法及应用[J].科学出版社,2000
[26]何东健,耿南.数字图像处理[M].西安电子科技大学出版社,2003
[27]KRCastleman. Digital Image Processing. Prentice Hall&清华大学出版社,1998
[28]Kass M,Witkinm A,Terzopoulos D,Snakes:Active contour models[J].International Journal on Computer Vision,1998,1(4):321-331.
[29]Won Kim,Choon Young Lee,Ju Jang Lee,Tracking moving object using Snake's Jump based on image flow[J].Mechanics,2001(11):119-216.
[30]Liu Y,Huang TS,Determining straight line correspondences from intensity images[J].Pattern Recognition,1991,24(6):489-504.
[31]Hang Z,Faugeras 0 D,Three dimensional motion computation and object segmentation in a long sequence of stereo frames[J].International Journal on Computer Vision,1992,7(3):211-241.
[32]Ferruz J,Ollero A,Integrated real time vision system for vehicle control in non-structured environments[J].Engineering Applications of Artificial Intelligence.2000(13):215-236.
[33]Jung SK,Wohn KY,a model based 3D tracking of rigid objects from a sequence of multiple perspective views[J].Pattern Recognition on Leters,1998(19):499-512
[34]J.Huang,S.Kumar,M.Mitra,et al,Spatial Color Indexing and Applications.Computer Vision,1999,35(3),245-268
[35]D.Comaniciu,V Ramesh,P.Meer.Real-Time Tracking of Non-Rigid Objects Using Mean Shift.Proc.IEEE Conf.Computer Vision and Pattern Recognition,2000,6(2):142-149
[36]Yizong Cheng.Mean Shift,Mode Seeking,and Clustering.IEEE Transactions on Patterns and Machine Intelligence.1995,8(17):790-799.
[2]Yasushi Mae,Yoshiaki Shirai.Jun Miura,et.Object tracking in cluttered background based on optical flow and edges[EB/OL].http://www.cv.mech.eng.o-sakau.ac.jp/research/ tracking-group/mae/flow-edge/ICPR96/ICPR96.html
[3]Yasushi Mae,Shinya Yamamoto,Yoshiaki Shirai,et.Opticalflow based rea-ltimeobject tracking by active vision System[EB/OL].http://ww.cv.mech.eng.os-akau.ac.jp/research/ track2ing-group/mae/flow/JFCM94/JFCM94.html
[4]Ryuzo Okada,Yoshiaki Shirai,Jun Miura.Object tracking basedon opticalflowand depth[EB/OL].http://www.cv.mech.eng.osakau.ac.jp/research/tracking-group/okada/flow-disp-track2ing/flow-disp-tracking.html
[5]Shunsuke KAMIJO,Katsushi IKEUCHI,Masao SAKAUCHI. Vehicle Tracking in Low-angle and Front-View Images based on Spatio-Temporal Markov Random Field Model.8th WorldCongress 2001 ITS.Sudney,2001
[6]Yasushi Mae,Shinya Yamamoto,Yoshiaki Shirai,et.Opticalflow based rea-ltimeobject tracking by active vision System[EB/OL].http://www.cv.mech.eng.os-akau.ac.jp/research/ track2ing-group/mae/flow/JFCM94/JFCM94.html
[7]PREVEDOUROS P D, Ji Xiaojin; PAOANDEOU K. Video incidentdetection tests in freeway tunnels [EB/OL],http://pubsindex.trb.org/document/view/default.asp?lbid=77 6527,2009-2-25
[8]Michalopoulos PG. Vehicle detection video through image processing:the autoscope system [J].IEEE Trans. Vehicular Technology,1991,40(1):21-29
[9]Image Sensing Systems, Inc. (ISS).Autoscope Products[EB/OL],http://www.auto scope.com/,2009-02-30
[10]曹仁川,刘天宇,董铁军.全视频技术在电子警察系统中的应用[J].中国信息产业，2004，104-105
[11]张泽旭,李金宗,李宁宁.基于光流场分割和Canny边缘提取融合算法的运动目标检测[J].电子学报.2003,31(9)：1299-1302
[12]魏波.点时空约束图像目标跟踪理论与实时实现技术研究[D].西安：电子科技大学,2000.
[13]周正宇,汪亚明,黄文清等.基于遗传算法聚类的车辆跟踪[J].计算机工程与设计.2004,25(7)：1218-1222
[14]潘薇,游志胜,吴鲲等.基于模糊聚类和卡尔曼滤波的运动目标检测[J]计算机应用.2005,25(1)：123-124
[15]杨杨,张田文.一种基于特征光流的运动目标跟踪方法[J].宇航学报.2000,2(2)：8-15
[16]Gary Bradski,Adrian Kaebler.Learning OpenCV[M].2009
[17]朱秀昌,刘峰,胡栋.数字图像处理与图像通信[J].北京邮电大学出版社.
[18]Horn B,Schunck B.Determining optieal flow.Artificial Intelligence,1981,17:185-203
[19]Anderson C, Bert P, Vander Wal G. Change detection and tracking using pyramids transformation techniques[A].In:Proceedings of SPIE Conference on Intelligent Robots and Computer Vision [C] Cambridge, MA, USA,1985,579:72-78.
[20]Lipton A, Fujiyoshi H, Patil R. Moving target classification and tracking from real-time video[A].In:Proceedings of IEEE Workshop on Applications of Computer Vision[C], Princeton,N J,US A,1998:8-14.
[21]衣秀清.静态场景下运动目标检测与跟踪算法研究[D].山东：山东大学硕士学位论文,2007：7-35.
[22]HARITAOGLU I, DAVIS LARRY S etal.A real time system for detecting and tracking people[A].In:Proceedings of IEEE International Conference on Automatic Face and Geture Recognition. Nara, Japan,1998 [C].
[23]徐瑞鑫.复杂背景下实时目标跟踪与识别研究[D].长春：中国科学院研究生院硕士学位论文,2002：7-14.
[24]N.Otsu.A Threshold Selection Method from Gray-Level Histograms[J].IEEE Trans-actions on Systems,Man and Cybernetics,1979,9(1):62-66.
[25]崔屹.图像处理与分析—数学形态学方法及应用[J].科学出版社,2000
[26]何东健,耿南.数字图像处理[M].西安电子科技大学出版社,2003
[27]KRCastleman. Digital Image Processing. Prentice Hall&清华大学出版社,1998
[28]Kass M,Witkinm A,Terzopoulos D,Snakes:Active contour models[J].International Journal on Computer Vision,1998,1(4):321-331.
[29]Won Kim,Choon Young Lee,Ju Jang Lee,Tracking moving object using Snake's Jump based on image flow[J].Mechanics,2001(11):119-216.
[30]Liu Y,Huang TS,Determining straight line correspondences from intensity images[J].Pattern Recognition,1991,24(6):489-504.
[31]Hang Z,Faugeras 0 D,Three dimensional motion computation and object segmentation in a long sequence of stereo frames[J].International Journal on Computer Vision,1992,7(3):211-241.
[32]Ferruz J,Ollero A,Integrated real time vision system for vehicle control in non-structured environments[J].Engineering Applications of Artificial Intelligence.2000(13):215-236.
[33]Jung SK,Wohn KY,a model based 3D tracking of rigid objects from a sequence of multiple perspective views[J].Pattern Recognition on Leters,1998(19):499-512
[34]J.Huang,S.Kumar,M.Mitra,et al,Spatial Color Indexing and Applications.Computer Vision,1999,35(3),245-268
[35]D.Comaniciu,V Ramesh,P.Meer.Real-Time Tracking of Non-Rigid Objects Using Mean Shift.Proc.IEEE Conf.Computer Vision and Pattern Recognition,2000,6(2):142-149
[36]Yizong Cheng.Mean Shift,Mode Seeking,and Clustering.IEEE Transactions on Patterns and Machine Intelligence.1995,8(17):790-799.