杨建全,梁华,王成友

• 1:国防科学技术大学电子科学与工程学院智能感知系统联合研究中心
• 2:国防科学技术大学电子科学与工程学院智能感知系统联合研究中心
• 3:国防科学技术大学电子科学与工程学院智能感知系统联合研究中心 湖南长沙410073
• 4:湖南长沙410073

摘要(Abstract)：

视频监控技术为重要场所安全防护提供了可靠保障,随着视频监控系统应用领域的扩大,视频监控技术得到了迅速发展。首先对视频监控技术的发展历程分三个阶段进行了概括,其次对视频监控中的关键技术的现状和发展方向进行了分析,其中重点描述了运动检测中的主流方法———背景差方法,目标跟踪中用于解决遮挡问题的合并分裂方法和直接穿透方法,最后提出了视频监控技术在实际应用中所面临的问题。

关键词(KeyWords)： 视频监控技术;运动检测;目标跟踪;目标分类

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金资助项目(60334010,60475029)

作者(Author): 杨建全,梁华,王成友

参考文献(References)：

• [1]Boghossian B,Velastin S.Image Processing System for Pe-destrian Monitoring Using Neural Classification of NormalMotion Patterns[J].Measurement and Control,1999,32(9):261 264.
• [2]Boghossian B,Velastin S.Motion based Machine VisionTechniques for the Management of Large Crowds[C].IEEE6th International Conference on Electronics,Circuits andSystems,Pafos,Cyprus,1999.
• [3]Collins R.A System for Video Surveillance and Monitoring:VSAM Final Report.Carnegie Mellon University,TechnicalReport:CMU RI TR 00 12,2000.
• [4]Omar Javed,Mubarak Shah.KNIGHTM A Real Time Sur-veillance System.http://www.cs.ucf.edu/vision/papers/Javed Shah ITS2003.pdf.
• [5]Arun Hampapur.S3 R1:The IBM Smart Surveillance Sys-tem Release 1.IBM T.J.Watson Research Center.
• [6]Remagnino P,Tan T,Baker K.Multi agent Visual Surveil-lance of Dynamic Scenes[J].Image and Vision Computing,1998,16(8):529 532.
• [7]Manzanera A,Richefeu J.A Robust and Computationally Ef-ficient Motion Detection Algorithm Based onΣΔBackground Estimation.In ICVGIP,Kolkata,India,2004.
• [8]Richefeu J,Manzanera A.A New Hybrid Differential Filterfor Motion Detection.ICCVG′04,Warsaw,Poland,2004:22 24.
• [9]Stauffer C,Grimson E.Learning Patterns of Activity UsingReal Time Tracking[J].IEEE Transactions on PatternRecognition and Machine Intelligence(TPAMI),2000,22(8):747 757.
• [10]Stauffer C,Grimson W E L.Adaptive Background MixtureModels for Real Time Tracking.in Proc.Computer Visionand Pattern Recognition 1999(CVPR′99),1999.
• [11]Elgammal A,Harwood D,Davis L.Non parametric Modelfor Background Subtraction.in Proc.of ICCV′99 Framerate Workshop,1999.
• [12]Oliver N M,Rosario B,Pentland A P.A Bayesian Comput-er Vision System for Modeling Human Interactions[J].IEEE Trans.on Patt.Anal.and Machine Intell.,2000,22(8):831 843.
• [13]Han B,Comaniciu D,Davis L.Sequential Kernel DensityApproximation through Mode Propagation:Applications toBackground Modeling[C].Proc.ACCV Asian Conf.onComputer Vision,2004.
• [14]Haritaoglu I,Harwood D,Davis L S.W4:Real Time Sur-veillance of People and Their Activities[J].IEEE Trans.Pattern Analysis and Machine Intelligence,2000,22(8):809 830.
• [15]Javed O,Shafique K,Shah M.A Hierarchical Approach toRobust Background Subtraction using Color and GradientInformation.IEEE Workshop on Motion and Video Compu-ting,Orlando,2002.
• [16]Javed O,Shah M.Tracking And Object Classification For Au-tomated Surveillance[C].The Seventh European Conference onComputer Vision(ECCV 2002),Copenhagen,2002.
• [17]Javed O,Rasheed Z,Alatas O,et al.KNIGHTM:A RealTime Surveillance System for Multiple Overlapping andNon Overlapping Cameras[C].IEEE International Con-ference on Multimedia and Expo(ICME 2003),Baltimore,Maryland,USA,2003.
• [18]Cucchiara R,Grana C,Piccardi M,et al.Detecting MovingObjects,Ghosts and Shadows in Video Streams[J].IEEETrans.on Patt.Anal.and Machine Intell.,2003,25(10):1 337 1 342.
• [19]Cucchiara R,Grana C,Piccardi M,et al.Detecting Objects,Shadows and Ghosts in Video Streams by Exploiting Colorand Motion Information[A].Proc of IEEE Int′l Conferenceon Image Analysis and Processing[C].[s.l.]:IEEE,2001:360 365.
• [20]Tian Y L,Lu M,Hampapur A.Robust and Efficient Fore-ground Analysis for Real Time Video Surveillance.CVPR,2005,(1):1 182 1 187.
• [21]Koller D,Danilidis K,Nagel H.Model based ObjectTracking in Monocular Image Sequences of Road TrafficScenes[J].Int.J.Comput.Vis.,1993,10(3):257 281.
• [22]Gu C,Lee M C.Semiautomatic Segmentation and Trackingof Semantic Video Objects[J].IEEE Trans.Circuits Syst.Video Technol.,1998,8(5):572 584.
• [23]Zhao J W,Wang P,Liu C Q.An Object Tracking Algo-rithm Based on Occlusion Mesh Model[C].in Proc.Int.Conf.Machine Learning and Cybernetics,2002:288 292.
• [24]Sun S,Haynor D R,Kim Y.Semiautomatic Video ObjectSegmentation Using VSnakes[J].IEEE Trans.CircuitsSyst.Video Technol.,2003,13(1):75 82.
• [25]Jang D S,Choi H I.Active Models for Tracking MovingObjects[J].Pattern Recognition,2000,33(7):1 135 1 146.
• [26]Segen J,Pingali S.A Camera based System for TrackingPeople in Real Time[C].Proc International Conference onPattern Recognition,Vienna,1996:63 67.
• [27]Pierre F Gabriel,Jacques G Verly,Justus H Piater,et al.The State of the Art in Multiple Object Tracking UnderOcclusion in Video Sequences.
• [28]Haritaoglu I.A Real Time System for Detection and Track-ing of People and Recognizing Their Activities.PHD Pro-posal,University of Maryland,1998.
• [29]Piater J H,Crowley J L.Multi modal Tracking of Interac-ting Targets Using Gaussian Approximations[M].SecondIEEE International Workshop on Performance Evaluationof Tracking and Surveillance,2001.
• [30]McKenna S,Jabri S,Duric Z,et al.Tracking Groups ofPeople[M].Computer Vision and Image Understanding,2000.
• [31]Brémond F,Thonnat M.Tracking Multiple Nonrigid Ob-jects in Video Sequences.IEEE Trans.on Circuits and Sys-tems for Video Techniques,1998.
• [32]Elgammal A,Davis L S.Probabilistic Framework for Seg-menting People under Occlusion[C].Proc.of IEEE 8th In-ternational Conference on Computer Vision,2001.
• [33]Senior A W,Hampapur A,Brown L M,et al.AppearanceModels for Occlusion Handling.in 2nd International Work-shop on Performance Evaluation of Tracking and Surveil-lance Systems,2001.
• [34]Khan S,Shah M.Tracking People in Presence of Occlusion[C].in Asian Conference on Computer Vision,2000.
• [35]Roh H K,Lee S W.Multiple People Tracking Using a Ap-pearance Model Based on Temporal Color[C].in Interna-tional Conference on Pattern Recognition,2000.
• [36]Rosales R,Sclaroff S.Improved Tracking of Multiple Hu-mans with Trajectory Prediction and Occlusion Modeling[M].in Proc.IEEE Workshop on the Interpretation of Vis-ual Motion,1998.
• [37]Dockstader S L,Tekalp A M.Tracking Multiple Objects inthe Presence of Articulated and Occluded Motion[M].inProc.Workshop Human Motion,2000.
• [38]Isard M,Blake A.Condensation:Conditional Density Prop-agation for Visual Tracking.in International Journal ofComputer Vision,1998.
• [39]Li P,Zhang T,Pece A E C.Visual Contour Tracking Basedon Particle Filters[M].in Image and VisionComputing,2003.
• [40]Nummiaro K,Koller Meier E,Van Gool L.A Colorbased Particle Filter.in First International Workshop onGenerative Model based Vision,2002.
• [41]Ok H W,Seo Y,Hong K S.Multiple Soccer Players Track-ing by Condensation with Occlusion Alarm Probability[M].International Workshop on Statistical Methods forVision Processing,2002.
• [42]MacCormick J,Blake A.A Probabilistic Exclusion Principlefor Tracking Multiple Objects.in International Journal ofComputer Vision,2000.
• [43]Ross Cutler,Larry S Davis.Robust Real Time PeriodicMotion Detection,Analysis,and Applications[J].IEEETransactions on Pattern Analysis and Machine Intelli-gence,2000,22(8).
• [44]Liu Bo,Zhou Heqin.Using Object Classification to ImproveUrban Traffic Monitoring System.IEEE Int Conf.NeuralNetworks&Signal Processing,2003.
• [45]Narasimhan S G,Nayar S K.Chromatic Framework for Vi-sion in Bad Weather[J].Proc.IEEE Computer Vision andPattern Recognition,2000:1 598 1 605.
• [46]Kettnaker V,Zabih R.Bayesian Multi camera Surveillance[J].Proc.IEEE Computer Vision and Pattern Recognition,1999:253 259.
• [47]Kelly P,Katkere A,Kuramura D,et al.An Architecture forMultiple Perspective Interactive Video.Proceedings of the3rd ACM International Conference on Multimedia,1995:201 212.
• [48]Cai Q,Aggarwal J.Tracking Human Motion Using Multi-ple Cameras[C].Proc.International Conference on PatternRecognition,1996:68 72.
• [49]Huang T,Russell S.Object Identification in a BayesianContext[J].Proceesings of IJCAI,1997:1 276 1 283.
• [50]Hanna Pasula,Stuart J Russell,Michael Ostland,et al.Tracking Many Objects with Many Sensors[J].Proceed-ings of IJCAI,1999:1 160 1 171.