静止背景下运动目标跟踪算法分析比较
摘要
本文综述了静止背景下,介绍运动目标跟踪的应用背景,理论与方法,文章主要是对人的运动图像序列的分析处理,对所涉及到的运动目标检测,运动目标分类,目标跟踪几部分进行了详细的阐述,并列举一些常见的算法,分类介绍各种算法的优缺点,并讨论一些具体问题的优化技术,如遮挡问题,阴影问题,实时性问题等。最后总结视觉跟踪的未来发展前景及努力的研究方向。
Computer Vision is an independent discipline,Computer Vision's goal is to make a computer through images ( compared static images, sports images include more information) have a ability of Cognitiving the surrounding environment information,This capability not only makes the computer can sense objects'geometric information, including its shape,location,posture, movement,but also describes them,storage,identiflcation and understanding. Movement of visual analysis in the field of computer vision in recent years been concerned,Based on the Vision Campaign Analysis,one of the potential applications is visual monitoring,which not only has a high scientific value but also has a huge potential economic value.
This paper is about motion analysis .There are three questions: motion detection,classification and the target tracking.
Moving target detection's purpose is extracting change image sequences from the background image.Several of these methods including: 1) background subtraction 2) Difference between frames, the frame difference Improvement Act 3) optical flow,background image estimation methods including:Statistical Average,IIR Filter, Adaptive Gaussian Background Model;there are many threshold selection method.Having the threshold segmentation algorithm,Ostu algorithm,dual-threshold method,and so on.Target detection steps can be divided into 1) preconditioning 2) Background Model 3) prospects for detection 4) post-processing.Object pretreatment methods: Neighborhood average method,median filtering method,the paper also describes the mathematical morphology.
Target Classification will get an unknown target from observation datas, with a group of suitable characteristics as input and outputing target category or the possibility of different types of objectives.This paper briefly describes the objective classification:based on the shape of information classification, based on the characteristics of sports classification.
Moving target tracking in the image sequence needs to get a target location in each image,based on the goals and objectives of the environment in which,select one or more targets characteristics which can distinguish target from the background.Moving target tracking can not only provide the trajec-tory,as well as provide a reliable source of data for moving target Scene Analysis and Motion Analysis,and moving target tracking information has helped in the correct moving target detection and identification of target movement. For tracking process, Scene assumptions, Moving Target Type, the campaign features moving target, cameras and camera movement, deformation objects, background changes, irregular objects Campaign in the campaign process and the color and shape, the tracking methods change vary widely. The difficulty of tracking is how fast and reliable images match goal from one frame to another image.Fast can guarantee real-time tracking, and reliability of tracking is the most basic requirement. Moving target tracking method can be summarized as follows: 1) Based on the regional matching,which combined with least squares fitting and template matching,will be effective in reducing the amount of computation,improving real-time.2) Based on the feature matching.3) Based on the model matching.
Finally,various methods in this article are compared and the paper discusses a number of specific issues,such as shielding,shadows,real-time optimiza- tion technology and so on,and summarizes the visual tracking development directions and prospects.
Computer Vision is an independent discipline,Computer Vision's goal is to make a computer through images ( compared static images, sports images include more information) have a ability of Cognitiving the surrounding environment information,This capability not only makes the computer can sense objects'geometric information, including its shape,location,posture, movement,but also describes them,storage,identiflcation and understanding. Movement of visual analysis in the field of computer vision in recent years been concerned,Based on the Vision Campaign Analysis,one of the potential applications is visual monitoring,which not only has a high scientific value but also has a huge potential economic value.
This paper is about motion analysis .There are three questions: motion detection,classification and the target tracking.
Moving target detection's purpose is extracting change image sequences from the background image.Several of these methods including: 1) background subtraction 2) Difference between frames, the frame difference Improvement Act 3) optical flow,background image estimation methods including:Statistical Average,IIR Filter, Adaptive Gaussian Background Model;there are many threshold selection method.Having the threshold segmentation algorithm,Ostu algorithm,dual-threshold method,and so on.Target detection steps can be divided into 1) preconditioning 2) Background Model 3) prospects for detection 4) post-processing.Object pretreatment methods: Neighborhood average method,median filtering method,the paper also describes the mathematical morphology.
Target Classification will get an unknown target from observation datas, with a group of suitable characteristics as input and outputing target category or the possibility of different types of objectives.This paper briefly describes the objective classification:based on the shape of information classification, based on the characteristics of sports classification.
Moving target tracking in the image sequence needs to get a target location in each image,based on the goals and objectives of the environment in which,select one or more targets characteristics which can distinguish target from the background.Moving target tracking can not only provide the trajec-tory,as well as provide a reliable source of data for moving target Scene Analysis and Motion Analysis,and moving target tracking information has helped in the correct moving target detection and identification of target movement. For tracking process, Scene assumptions, Moving Target Type, the campaign features moving target, cameras and camera movement, deformation objects, background changes, irregular objects Campaign in the campaign process and the color and shape, the tracking methods change vary widely. The difficulty of tracking is how fast and reliable images match goal from one frame to another image.Fast can guarantee real-time tracking, and reliability of tracking is the most basic requirement. Moving target tracking method can be summarized as follows: 1) Based on the regional matching,which combined with least squares fitting and template matching,will be effective in reducing the amount of computation,improving real-time.2) Based on the feature matching.3) Based on the model matching.
Finally,various methods in this article are compared and the paper discusses a number of specific issues,such as shielding,shadows,real-time optimiza- tion technology and so on,and summarizes the visual tracking development directions and prospects.
引文
[1]Meyer D,Denzler J,Niemann H.Model based extraction of articulated objects in image sequences for gait analysis.In:Proc IEEE International Conference on Image Processing,Santa Barbara,California 1997,78-81.
[2]Mclachlan G,Krishnan T.The EM Algorithm and Extensions.WileyInterscience,1997.
[3]Stringa E.Morphological change detection algorithms for surveillance applications.In:Proc British Machine Vision Conference,Bristol,UK,2000,402-411.
[4]Collins R Letall.A system for video surveillance and monitoring:VASM final report.Carnegie Mellon University:Technical Report CMU-RI-TR-OO-12,2000.
[5]Lipton H,Fujiyoshi H,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.
[6]Kuno Y,Watanabe T,Shimosakoda Y,Nakagawa S.Automated detection of human for Visual Surveillance system.In:Proc IEEE International Conference on Pattern Recognition,Vienna,1996.865-869.
[7]Cutler R,Davis L.Robust real-time periodic motion detection,analysis,and applications.IEEE Trans Pattern Analysis and Machine Intelligence,2000,22(8):781-796.
[8]Lipton A.Local application of optic flow to analyse rigid versus non-rigid motion.In:http://www.eecs.lehigh.edu/FRAME/Lipton/iccvframe.html
[9]Stauffer C.Automatic hierachical classification using time-base cooccurrences.In:Proc IEEE CS Conference on Computer Vision and Pattern Recognition,1999,333-339.
[10]Boghossian B,Velastin S.Image processing system for pedestrian monitoring using neural classification of normal motion patterns.Measurement and Control,1999,32(9):261-264.
[11]Boghossian B,Velastin S.Motion-based machine vision techniques for the management of large crowds.In:Proc IEEE 6th International Conference on Electronics,Circuits and Systems,Pafos,Cyprus,1999.
[12]Gavrila D.The visual analysis of human movement:a survey.Computer Vision and Image Understanding,1999,73(1):82-98.
[13]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,3(16):1-8.
[14]Collins R,Lipton A and Kanade T.Introductionto the special section on video urveillance.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,8(22):745-746.
[15]Maybank S and Tan T.Introduction-special section on visual surveillance.International Journal of Computer Vision,2000,37(2):173-173.
[16]刘亚,艾海舟,徐光佑.一种基于背景模型的运动目标检测与跟踪算法.信息与控制,2002,4(31):315-319.
[17]C.Wren,A.Azarbayejani,T.Darrell,and A.Pentland.Pfinder:Real-time Tracking of the Human Body.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,7(19):780-785.
[18]T.Olson,F.Brill,Moving Object Detection and Event Recognition Algorithms for Smart Cameras.Proc.DARPA Image Understanding Workshop,1997,3.
[19]I.Haritaoglu,D.Harwood and L.S.Davis.W4:Rea-Time Surveillance of People and Their Activities.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,8(22):809-830.
[20]Collins R,Lipton A and Kanade T.A System for Video Surveillance and Monitoring.Reports of DARPA Image Understanding,1998.4.
[21]Remagnino P,Tan T and Baker K.Multi-agent visual surveillance of dynamic scenes.Image and Vision Computing,1998,16(8):529-532.
[22]Collins Retal.A system for video surveillance and monitoring:VSAM final report.Carnegie Mellon University,Technical Report:CMU-RI-TR-00-12,2000.
[23]张文杰,戚飞虎,江卓军.实时视频监控系统中运动目标检测和跟踪的一种实用方法.上海交通大学学报,2002,12(36):1834-1840.
[24]McKenna Setal,Tracking groups of people.Computer Vision and Image Understanding,2000,80(1):42-56.
[25]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.
[26]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.
[27] Stauffer C and Grimson W. Adaptive background mixture models for realtime tracking In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 1999, 2:246-252.
[28] 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.
[29] 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.
[30] Azarbayejani A and Pentland A. Real-time self-calibrating stereo person tracking using 3D shape estimation from blob features .In:Proc International Conference on Pattern Recognition, Vienna, 1996, 627-632.
[31] Cai Q, Mitiche A and Aggarwat J. Tracking human motions in an indoor environment.In: Proc International Conference on Image Processing, Washington, DC, USA, 1995, 1:215-218.
[32] 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.
[33] Rehg J and Kanade T.Model-based tracking of self-occluding articulated objects.In: Proc the 5th International Conference on Computer Vision, Cambridge, 1995, 612-617.
[34] Ricquebourg Y and Bouthemy P.Real-time tracking of moving persons by exploiting spatio-temporal image slices. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22(8):797-808.
[35]Chang T,Gong S and Ong E.Tracking multiple people under occlusion using multiple cameras.In:British Machine Vision Conference,Bristol,UK,2000,566-575.
[36]Boult T.Frame-rate multi-body tracking for surveillance.In:Proc DARPA Image Understanding Workshop,Monterey,Calif.San Francisco:Morgan Kaufmann,1998.
[37]Niyogi S and Adelson E.Analyzing and recognizing walking figures in XYT.In:Proc IEEE Conference on Computer Vision and Patern Recognition,Seattle,USA,1994,469-474.
[38]侯格贤,唐明,马颂德.图像中目标的快速搜索算法[J].中国图像图形学报,2001,6(8):760-764.
[39]Kass M,Witkin A,Terzopoulos D.Snakes:Active Contour Models.In.Proc 1st Int Conf on Computer Vision,London,1987.259-268.
[40]张桂林,徐捷.频域相关技术在图像匹配中的应用[J].模式识别与人工智能,1997,10(1):87-92.
[41]Zitova B,Flusser J.Image registration methods:a survey[J].Image and Vision Computing,2003,21:977-1000.
[42]Cutler R,Paris L.Robust Real-time periodic motion detection analysis,and application.IEEE Trans Pattern Analysis and Machine Intelligence,2000,22(8):781-796.
[43]Lipton A.Local application of optic flow to analyse rigid versus non-rigid motion.Tech.report CMU-RI-TR-99-13,Robotics Institute,Carnegie Mellon University,http://www.eecs.Iehigh.edu/FRAME/Liption/iccvframe.html.1999-9.
[44]Y.Bogomolov,G.Dror.Classification of moving targets based on motion and appearance.In:Machine Vision Conference.Rudzsky British:2003,429-435.
[45]Hong L.Multiresolutional Estimation Using Wavelet Transform[J].IEEE Trans.on Aero Space and Electronic System,1993,29:1244-1251.
[46]David A.Forsyth,Jean Ponce.计算机视觉—一种现代方法.电子工业出版社,2004.6.
[47]Anil K.Jain,数字图像处理基础.清华大学出版社,2006.11.
[48]皮文凯,刘宏,查红彬.基于自适应背景模型的全方位视觉人体运动检测.北京大学学报(自然科学版),2004(3):119-125.
[49]张继平,刘直芳.背景估计与运动目标检测跟踪.计算机技术与自动化,2004(4):51-54.
[50]周兵,李晓强,苏士美.实用户外视频监控异常检测策略.计算机工程,2003.9.
[51]杜峥,张桂林,洪靖云.视频监控系统中的一种运动检测算法.机械与电子,2003.4.
[52]左国辉,王金刚,靳晓辉.运动检测算法.电子测量技术,2005.
[53]黄薇,肖平,冯刚.一种具有良好完整性的运动检测与跟踪方法.计算机与数字工程,2006.
[54]Wong AK,Sahoc PK.A gray-level threshold selection method based on maximum entropy principle.IEEE Trans.1989.SMC-19(4):866-871.
[55]Kaput JK,Sahoo PK,Wong KC.A new method for gray-level picture thresholding using the entropy of the histogram.Computer Vision,Graphics and Image Processing.1985,29(2):273-285.
[56]景晓军等.一种基于二维最大类间方差的图像分割算法.通信学报,2001,22(4):71-76.
[57]李超,熊璋,赫阳,刘玉恒.基于帧间差的区域光流分析及其应用.计算机工程与应用.2005.2.
[58]邱德润,朱明旱,伍宗富.一种基于帧间差分与背景差分的运动目标检测新方法.湖南文理学院学报(自然科学版),2007.3.
[59]丁金铸,淡国新.基于视频序列中的目标检测技术.湖南工程学院学报,2007.9.
[60]朱明旱,罗大庸.基于帧间差分背景模型的运动物体检测与跟踪.计算机测量与控制,2006.14(8).
[61]崔屹,图像处理与分析—数学形态学方法及应用.科学出版社.2002.
[62]邵文坤,黄爱民韦庆.目标跟踪方法综述.影像技术,2006.1.
[63]郑林,韩崇昭,左东广,王永昌.基于特征融合的运动目标识别.系统仿真学报,2004.5.
[64]高珏,常永亮,穆战松.基于运动目标识别技术的视频监控系统.计算机技术与自动化,2005.12.
[65]隋晔.交通监控系统中运动目标分类和跟踪研究.信息与控制,2003.2.
[66]王海霞,徐欣锋,徐抒岩.一种基于不变矩的目标识别方法.光学精密工程,2004.8.
[67]方帅,王东署,迟健男,徐心和.视频监控系统中小目标分类算法.信息与控制,2005.4.
[68]杨耿,和卫星.运动目标图像识别与跟踪系统的研究.计算机测量与控制,2005.13(3).
[69]孟庆华,沈振康,张忠诚.一种基于目标红外特征的目标分类识别方法.红外技术,1999.5.
[70]张志佳,张瑜,史泽林,黄莎白.基于区域模板相关的图像匹配方法研究.红外与激光工程,2004.12.
[71]刘铁军,肖颖杰,史泽林.区域匹配相关跟踪方法的一种改进.信息与控制,2001.7.
[72]吕艳玲,张建奇,张延伸.一种新的相关跟踪方法研究.光电技术应用,2005.10.
[73]王宾,潘建寿,王琳,崔宇巍.基于多特征融合的均值偏移视频目标跟踪算法.小型微型计算机系统,2006.9.
[74]郝志成,朱明,刘微.复杂背景下目标的快速提取与跟踪.吉林大学学报(工学版),2006.3.
[75]严柏军,郑链,王克勇.基于不变矩特征匹配的快速目标检测算法.红外技术,2001.11.
[76]高红亮,郭如.基于边缘轮廓的特征匹配跟踪.激光与红外,2005.4.
[77]李国栋,刘春阳,柳长安.一种基于特征的动态目标检测与跟踪算法.哈尔滨工业大学学报,2005.7.
[78]杨杨,张田文.一种新的主动轮廓线跟踪算法.计算机学报,1998.8.
[79]牛君,李贻斌,宋锐.活动轮廓模型综述.杭州电子科技大学学报,2005.6.
[80]曹远星,董育宁.蛇模型综述.信息技术,2006.3.
[81]秦暄,章毓晋.一种基于曲线拟合预测的红外目标的跟踪算法.红外技术,2003.7.
[82]Aggarwal,J.K,and Cai Q.Human motion analysis:A review.Comput.Vision Image Understand,1999,428-440.
[2]Mclachlan G,Krishnan T.The EM Algorithm and Extensions.WileyInterscience,1997.
[3]Stringa E.Morphological change detection algorithms for surveillance applications.In:Proc British Machine Vision Conference,Bristol,UK,2000,402-411.
[4]Collins R Letall.A system for video surveillance and monitoring:VASM final report.Carnegie Mellon University:Technical Report CMU-RI-TR-OO-12,2000.
[5]Lipton H,Fujiyoshi H,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.
[6]Kuno Y,Watanabe T,Shimosakoda Y,Nakagawa S.Automated detection of human for Visual Surveillance system.In:Proc IEEE International Conference on Pattern Recognition,Vienna,1996.865-869.
[7]Cutler R,Davis L.Robust real-time periodic motion detection,analysis,and applications.IEEE Trans Pattern Analysis and Machine Intelligence,2000,22(8):781-796.
[8]Lipton A.Local application of optic flow to analyse rigid versus non-rigid motion.In:http://www.eecs.lehigh.edu/FRAME/Lipton/iccvframe.html
[9]Stauffer C.Automatic hierachical classification using time-base cooccurrences.In:Proc IEEE CS Conference on Computer Vision and Pattern Recognition,1999,333-339.
[10]Boghossian B,Velastin S.Image processing system for pedestrian monitoring using neural classification of normal motion patterns.Measurement and Control,1999,32(9):261-264.
[11]Boghossian B,Velastin S.Motion-based machine vision techniques for the management of large crowds.In:Proc IEEE 6th International Conference on Electronics,Circuits and Systems,Pafos,Cyprus,1999.
[12]Gavrila D.The visual analysis of human movement:a survey.Computer Vision and Image Understanding,1999,73(1):82-98.
[13]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,3(16):1-8.
[14]Collins R,Lipton A and Kanade T.Introductionto the special section on video urveillance.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,8(22):745-746.
[15]Maybank S and Tan T.Introduction-special section on visual surveillance.International Journal of Computer Vision,2000,37(2):173-173.
[16]刘亚,艾海舟,徐光佑.一种基于背景模型的运动目标检测与跟踪算法.信息与控制,2002,4(31):315-319.
[17]C.Wren,A.Azarbayejani,T.Darrell,and A.Pentland.Pfinder:Real-time Tracking of the Human Body.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,7(19):780-785.
[18]T.Olson,F.Brill,Moving Object Detection and Event Recognition Algorithms for Smart Cameras.Proc.DARPA Image Understanding Workshop,1997,3.
[19]I.Haritaoglu,D.Harwood and L.S.Davis.W4:Rea-Time Surveillance of People and Their Activities.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,8(22):809-830.
[20]Collins R,Lipton A and Kanade T.A System for Video Surveillance and Monitoring.Reports of DARPA Image Understanding,1998.4.
[21]Remagnino P,Tan T and Baker K.Multi-agent visual surveillance of dynamic scenes.Image and Vision Computing,1998,16(8):529-532.
[22]Collins Retal.A system for video surveillance and monitoring:VSAM final report.Carnegie Mellon University,Technical Report:CMU-RI-TR-00-12,2000.
[23]张文杰,戚飞虎,江卓军.实时视频监控系统中运动目标检测和跟踪的一种实用方法.上海交通大学学报,2002,12(36):1834-1840.
[24]McKenna Setal,Tracking groups of people.Computer Vision and Image Understanding,2000,80(1):42-56.
[25]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.
[26]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.
[27] Stauffer C and Grimson W. Adaptive background mixture models for realtime tracking In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 1999, 2:246-252.
[28] 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.
[29] 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.
[30] Azarbayejani A and Pentland A. Real-time self-calibrating stereo person tracking using 3D shape estimation from blob features .In:Proc International Conference on Pattern Recognition, Vienna, 1996, 627-632.
[31] Cai Q, Mitiche A and Aggarwat J. Tracking human motions in an indoor environment.In: Proc International Conference on Image Processing, Washington, DC, USA, 1995, 1:215-218.
[32] 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.
[33] Rehg J and Kanade T.Model-based tracking of self-occluding articulated objects.In: Proc the 5th International Conference on Computer Vision, Cambridge, 1995, 612-617.
[34] Ricquebourg Y and Bouthemy P.Real-time tracking of moving persons by exploiting spatio-temporal image slices. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22(8):797-808.
[35]Chang T,Gong S and Ong E.Tracking multiple people under occlusion using multiple cameras.In:British Machine Vision Conference,Bristol,UK,2000,566-575.
[36]Boult T.Frame-rate multi-body tracking for surveillance.In:Proc DARPA Image Understanding Workshop,Monterey,Calif.San Francisco:Morgan Kaufmann,1998.
[37]Niyogi S and Adelson E.Analyzing and recognizing walking figures in XYT.In:Proc IEEE Conference on Computer Vision and Patern Recognition,Seattle,USA,1994,469-474.
[38]侯格贤,唐明,马颂德.图像中目标的快速搜索算法[J].中国图像图形学报,2001,6(8):760-764.
[39]Kass M,Witkin A,Terzopoulos D.Snakes:Active Contour Models.In.Proc 1st Int Conf on Computer Vision,London,1987.259-268.
[40]张桂林,徐捷.频域相关技术在图像匹配中的应用[J].模式识别与人工智能,1997,10(1):87-92.
[41]Zitova B,Flusser J.Image registration methods:a survey[J].Image and Vision Computing,2003,21:977-1000.
[42]Cutler R,Paris L.Robust Real-time periodic motion detection analysis,and application.IEEE Trans Pattern Analysis and Machine Intelligence,2000,22(8):781-796.
[43]Lipton A.Local application of optic flow to analyse rigid versus non-rigid motion.Tech.report CMU-RI-TR-99-13,Robotics Institute,Carnegie Mellon University,http://www.eecs.Iehigh.edu/FRAME/Liption/iccvframe.html.1999-9.
[44]Y.Bogomolov,G.Dror.Classification of moving targets based on motion and appearance.In:Machine Vision Conference.Rudzsky British:2003,429-435.
[45]Hong L.Multiresolutional Estimation Using Wavelet Transform[J].IEEE Trans.on Aero Space and Electronic System,1993,29:1244-1251.
[46]David A.Forsyth,Jean Ponce.计算机视觉—一种现代方法.电子工业出版社,2004.6.
[47]Anil K.Jain,数字图像处理基础.清华大学出版社,2006.11.
[48]皮文凯,刘宏,查红彬.基于自适应背景模型的全方位视觉人体运动检测.北京大学学报(自然科学版),2004(3):119-125.
[49]张继平,刘直芳.背景估计与运动目标检测跟踪.计算机技术与自动化,2004(4):51-54.
[50]周兵,李晓强,苏士美.实用户外视频监控异常检测策略.计算机工程,2003.9.
[51]杜峥,张桂林,洪靖云.视频监控系统中的一种运动检测算法.机械与电子,2003.4.
[52]左国辉,王金刚,靳晓辉.运动检测算法.电子测量技术,2005.
[53]黄薇,肖平,冯刚.一种具有良好完整性的运动检测与跟踪方法.计算机与数字工程,2006.
[54]Wong AK,Sahoc PK.A gray-level threshold selection method based on maximum entropy principle.IEEE Trans.1989.SMC-19(4):866-871.
[55]Kaput JK,Sahoo PK,Wong KC.A new method for gray-level picture thresholding using the entropy of the histogram.Computer Vision,Graphics and Image Processing.1985,29(2):273-285.
[56]景晓军等.一种基于二维最大类间方差的图像分割算法.通信学报,2001,22(4):71-76.
[57]李超,熊璋,赫阳,刘玉恒.基于帧间差的区域光流分析及其应用.计算机工程与应用.2005.2.
[58]邱德润,朱明旱,伍宗富.一种基于帧间差分与背景差分的运动目标检测新方法.湖南文理学院学报(自然科学版),2007.3.
[59]丁金铸,淡国新.基于视频序列中的目标检测技术.湖南工程学院学报,2007.9.
[60]朱明旱,罗大庸.基于帧间差分背景模型的运动物体检测与跟踪.计算机测量与控制,2006.14(8).
[61]崔屹,图像处理与分析—数学形态学方法及应用.科学出版社.2002.
[62]邵文坤,黄爱民韦庆.目标跟踪方法综述.影像技术,2006.1.
[63]郑林,韩崇昭,左东广,王永昌.基于特征融合的运动目标识别.系统仿真学报,2004.5.
[64]高珏,常永亮,穆战松.基于运动目标识别技术的视频监控系统.计算机技术与自动化,2005.12.
[65]隋晔.交通监控系统中运动目标分类和跟踪研究.信息与控制,2003.2.
[66]王海霞,徐欣锋,徐抒岩.一种基于不变矩的目标识别方法.光学精密工程,2004.8.
[67]方帅,王东署,迟健男,徐心和.视频监控系统中小目标分类算法.信息与控制,2005.4.
[68]杨耿,和卫星.运动目标图像识别与跟踪系统的研究.计算机测量与控制,2005.13(3).
[69]孟庆华,沈振康,张忠诚.一种基于目标红外特征的目标分类识别方法.红外技术,1999.5.
[70]张志佳,张瑜,史泽林,黄莎白.基于区域模板相关的图像匹配方法研究.红外与激光工程,2004.12.
[71]刘铁军,肖颖杰,史泽林.区域匹配相关跟踪方法的一种改进.信息与控制,2001.7.
[72]吕艳玲,张建奇,张延伸.一种新的相关跟踪方法研究.光电技术应用,2005.10.
[73]王宾,潘建寿,王琳,崔宇巍.基于多特征融合的均值偏移视频目标跟踪算法.小型微型计算机系统,2006.9.
[74]郝志成,朱明,刘微.复杂背景下目标的快速提取与跟踪.吉林大学学报(工学版),2006.3.
[75]严柏军,郑链,王克勇.基于不变矩特征匹配的快速目标检测算法.红外技术,2001.11.
[76]高红亮,郭如.基于边缘轮廓的特征匹配跟踪.激光与红外,2005.4.
[77]李国栋,刘春阳,柳长安.一种基于特征的动态目标检测与跟踪算法.哈尔滨工业大学学报,2005.7.
[78]杨杨,张田文.一种新的主动轮廓线跟踪算法.计算机学报,1998.8.
[79]牛君,李贻斌,宋锐.活动轮廓模型综述.杭州电子科技大学学报,2005.6.
[80]曹远星,董育宁.蛇模型综述.信息技术,2006.3.
[81]秦暄,章毓晋.一种基于曲线拟合预测的红外目标的跟踪算法.红外技术,2003.7.
[82]Aggarwal,J.K,and Cai Q.Human motion analysis:A review.Comput.Vision Image Understand,1999,428-440.