视频运动对象分割及其应用研究
|
摘要
视频运动对象分割是计算机视觉和视频处理领域一项关键技术,具有重要的研究和应用价值。本文针对单目、静止摄像机采集的视频,研究运动对象分割问题,取得的研究成果包括:
(1)提出一种基于均值漂移的背景建模及运动对象分割算法。通过均值漂移方法检测概率密度的模式,实现准确的背景估计。针对均值漂移计算复杂度高的局限性,根据帧间时域相关性,提出简化的均值漂移算法;同时通过基于四叉树结构的层次化方法减少逐像素检测造成的冗余计算,先在粗的尺度上搜索运动像素,再逐步以更细的尺度渐近优化运动物体分割。
(2)提出一种像素层背景模型及运动对象分割方法。本文利用背景运动局部性和时空变化相关性特征,将背景表示为一组具有相同统计特征的像素层,通过与像素邻域的层匹配来实现运动对象提取。本文在摄像机晃动等原因引起的像素时域变化不规则情况下,具有更稳定的分割效果。算法在空间与时间复杂度方面具有显著的优势。
(3)提出一种基于图切分的视频运动对象分割算法。首先建立基于像素层的背景模型,并在对视频帧初步分割的基础上建立前景和阴影模型。采用直方图统计的方法估计阴影对背景像素产生的衰减比例,以建立更准确的阴影模型。通过MRF随机场描述邻域像素间的空域一致性关系,利用图切分方法来求解视频运动对象分割问题。
在研究上述算法的基础上,开发了海事场景智能视频监控系统。系统从网络上获取海事场景的视频流,对用户定义的监控区域进行运动目标检测与跟踪,并根据用户定义的规则对异常事件进行告警。通过高效的运动目标分割与跟踪算法,以及分区域监控策略等方式来提高系统的速度,系统在微型计算机平台上实现多路视频的实时自动监控。
As a key supporting technique for computer vision,moving object segmentation has far-going pragmatism significance and application importance.This dissertation presents some efforts on extracting moving objects from monocular videos captured by static camera,and main contributions of my work include:
(1) Mean shift based background modeling and moving objects segmentation algorithm.Mean shift based non-parametric background modeling supports more sensitive and robust detection in dynamic outdoor scenes.This algorithm aims to deal with the limitation of high computational complexity.Firstly,fast mean shift approach is presented according to temporal dependencies.Secondly,coarse to fine method is proposed to avoid raster scanning entire image.Foreground pixels are detected in coarse level to roughly locate the foreground objects in the image,and then fine detection is performed on the corresponding blocks gradually.
(2) A background model based on pixel layer for moving objects segmentation.Fast mean shift approach is used to cluster into layers those pixels that share similar statistics.The background is then modeled as a group of pixel layers.An incoming pixel is detected as foreground if it does not adhere to these layer-models of the background.The proposed method performs better than the tradtional MoG method under temporally irregular dynamic textures.
(3) A moving objects segmentation algorithm based on graph cuts.The background model is represented as a group of pixel layers,and the foreground and shadow models are learned from background subtraction.We design a histogram based method to estimate darkening ratio caused by moving shadow so as to model the shadow more accurately.Markov Random Field is used to model the dependencies among neighbouring pixies,and the final foreground segmentation is subsequently achieved by the graph cuts algorithm.
We also developed an automatic video surveillance system for marine scenes.It accesses the video streams of marine scenes transferred through Internet and performs moving objects detection and tracking to discover the prohibited objects and alarm acording to user-defined rules.The system supports real time monitoring as many as eight channels of video stream on personal computer.
(1)提出一种基于均值漂移的背景建模及运动对象分割算法。通过均值漂移方法检测概率密度的模式,实现准确的背景估计。针对均值漂移计算复杂度高的局限性,根据帧间时域相关性,提出简化的均值漂移算法;同时通过基于四叉树结构的层次化方法减少逐像素检测造成的冗余计算,先在粗的尺度上搜索运动像素,再逐步以更细的尺度渐近优化运动物体分割。
(2)提出一种像素层背景模型及运动对象分割方法。本文利用背景运动局部性和时空变化相关性特征,将背景表示为一组具有相同统计特征的像素层,通过与像素邻域的层匹配来实现运动对象提取。本文在摄像机晃动等原因引起的像素时域变化不规则情况下,具有更稳定的分割效果。算法在空间与时间复杂度方面具有显著的优势。
(3)提出一种基于图切分的视频运动对象分割算法。首先建立基于像素层的背景模型,并在对视频帧初步分割的基础上建立前景和阴影模型。采用直方图统计的方法估计阴影对背景像素产生的衰减比例,以建立更准确的阴影模型。通过MRF随机场描述邻域像素间的空域一致性关系,利用图切分方法来求解视频运动对象分割问题。
在研究上述算法的基础上,开发了海事场景智能视频监控系统。系统从网络上获取海事场景的视频流,对用户定义的监控区域进行运动目标检测与跟踪,并根据用户定义的规则对异常事件进行告警。通过高效的运动目标分割与跟踪算法,以及分区域监控策略等方式来提高系统的速度,系统在微型计算机平台上实现多路视频的实时自动监控。
As a key supporting technique for computer vision,moving object segmentation has far-going pragmatism significance and application importance.This dissertation presents some efforts on extracting moving objects from monocular videos captured by static camera,and main contributions of my work include:
(1) Mean shift based background modeling and moving objects segmentation algorithm.Mean shift based non-parametric background modeling supports more sensitive and robust detection in dynamic outdoor scenes.This algorithm aims to deal with the limitation of high computational complexity.Firstly,fast mean shift approach is presented according to temporal dependencies.Secondly,coarse to fine method is proposed to avoid raster scanning entire image.Foreground pixels are detected in coarse level to roughly locate the foreground objects in the image,and then fine detection is performed on the corresponding blocks gradually.
(2) A background model based on pixel layer for moving objects segmentation.Fast mean shift approach is used to cluster into layers those pixels that share similar statistics.The background is then modeled as a group of pixel layers.An incoming pixel is detected as foreground if it does not adhere to these layer-models of the background.The proposed method performs better than the tradtional MoG method under temporally irregular dynamic textures.
(3) A moving objects segmentation algorithm based on graph cuts.The background model is represented as a group of pixel layers,and the foreground and shadow models are learned from background subtraction.We design a histogram based method to estimate darkening ratio caused by moving shadow so as to model the shadow more accurately.Markov Random Field is used to model the dependencies among neighbouring pixies,and the final foreground segmentation is subsequently achieved by the graph cuts algorithm.
We also developed an automatic video surveillance system for marine scenes.It accesses the video streams of marine scenes transferred through Internet and performs moving objects detection and tracking to discover the prohibited objects and alarm acording to user-defined rules.The system supports real time monitoring as many as eight channels of video stream on personal computer.
引文
[1]杨文明.时空联合的视频对象分割.浙江大学博士学位论文,2006.
[2]T.Ebrahimi,C.Home.MPEG-4 natural video coding-An overview.Signal Processing:Image Communication,2000,15(4-5):365-385.
[3]S.Chang,T.Sikora,A.Purl.Overview of the MPEG-7 standard.IEEE Transactions on Circuits and Systems for Video Technology,2001,11(6):688-695.
[4]孔晓东.智能视频监控技术研究.上海交通大学博士学位论文,2008.
[5]J.Aguilera,D.Thirde,M.Kampel,M.Borg,G.Fernandez,et al.Visual surveillance for airport monitoring applications.International Conference on Computer Communications and Networks,2005,255-262.
[6]D.M.Tsai,S.Lai.Independent component analysis-based background subtraction for indoor surveillance.IEEE Transactions on Image Processing,2009,18:158-167.
[7]W.Hu,X.Xiao,D.Xie,T.Tan,S.Maybank.Traffic accident prediction using 3-D model-based vehicle tracking.IEEE Transactions on Vehicular Technology,2004,53(3):677-694.
[8]W.Hu,T.Tan,L.Wang,S.Maybank.A survey on visual surveillance of object motion and behaviors.IEEE Transactions on Systems,Man and Cybernetics,2004,34(3):334-352.
[9]朱杰杰.增强现实中多视图几何问题的研究.浙江大学博士学位论文,2007.
[10]童晓峰,刘青山,卢汉清.体育视频分析.计算机学报,2008,31(7):1242-1251.
[11]吴思,张勇东,林守勋,李豪杰.动态场景视频序列中的前景区域自动提取.计算机辅助设计与图形学学报,2005,17(2):359-363.
[12]王嘉,王海峰,刘青山,卢汉清.基于三参数模型的快速全局运动估计.计 算机学报,2006,29(6):920-927.
[13]郭丽,龚声蓉,崔志明.一种快速全局运动估计算法.中国图象图形学报,2007,12(12):2119-2126.
[14]F.Dufaux,J.Konrad.Efficient,robust,and fast global motion estimation for video coding.IEEE Transactions on Image Processing,2000,9(3):497-501.
[15]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,25(3):225-237.
[16]A.J.Lipton,H.Fujiyoshi,R.Patil.Moving target classification and tracking from real-time video.IEEE Workshop on Applications of Computer Vision,1998,8-14.
[17]杨莉,张弘,李玉山.视频运动对象的自动分割.计算机辅助设计与图形学学报,2004,16(3):301-306.
[18]刘鑫,刘辉,强振平,耿续涛.混合高斯模型和帧间差分相融合的自适应背景模型.中国图象图形学报,2008,13(4):729-734.
[19]J.L.Barron,D.J.Fleet,S.Beauchemin.Performance of optical flow techniques.International Journal of Computer Vision,1994,12(1):43-47.
[20]杨威,张田文.复杂景物环境下运动目标检测的新方法.计算机研究与发展,1998,35(8):724-728.
[21]C.R.Wren,A.Azarbayejani,T.Darrell,A.Pentland.Pfinder:Real-time tracking of the human body.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):780-785.
[22]C.Stauffer,W.Grimson.Adaptive background mixture models for real-time tracking.IEEE Conference on Computer Vision and Pattern Recognition 1999,246-252.
[23]T.Matsuyama,T.Ohya,H.Habe.Background subtraction for non-stationary scenes.Asian Conference on Computer Vision,2000,662-667.
[24]A.Elgammal,R.Duraiswami,L.S.Davis.Efficient non-parametric adaptive color modeling using fast gauss transform.IEEE Conference on Computer Vision and Pattern Recognition,2001,563-570.
[25]A.Elgammal,R.Duraiswami,D.Harwood,L.S.Davis.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance.Proceedings of the IEEE,2002,90(7):1151-1163.
[26]O.Javed,K.Shafique,M.Shah.A hierarchical approach to robust background subtraction using color and gradient information.IEEE Workshop on Motion and Video Computing,2002,22-27.
[27]R.Cucchiara,C.Grana,M.Piccardi,A.Prati.Detecting moving objects,ghosts,and shadows in video streams.IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,10(8):1337-1342.
[28]D.Lee.Effective Gaussian Mixture Learning for Video Background Subtraction.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27:827-832.
[29]Q.Zhu,S.Avidan,K.Cheng.Learning a sparse,comer-based representation for background modelling.International Conference on Computer Vision,2005,678-685.
[30]L.Li,W.Huang,Y.Gu,Q.Tian.Statistical modeling of complex backgrounds for foreground object detection.IEEE Transactions on Image Processing,2004,13(11):1459-1472.
[31]R.Babu,K.Ramakrishnan,S.Srinivasan.Video object segmentation:a compressed domain approach.IEEE Transactions on Circuits and Systems for Video Technology,2004,14(4):462-474.
[32]孙涛,杨高波,刘理,张兆扬.一种基于梯度模型的MPEG压缩域的运动对象分割算法.中国图象图形学报,2008,13(6):1109-1114.
[33]W.Zeng,J.Du,W.Gao,Q.Huang.Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model.Real-Time Imaging,2005,11(4):290-299.
[34]杨高波,张兆扬.MPEG-4框架下的视频对象分割及其关键技术分析.通信 学报,2007,28(10):109-122.
[35]高欣,安平,刘佳,张兆扬.基于视差和变化检测的立体视频对象分割.上海大学学报:自然科学版,2006,12(2):116-119.
[36]朱仲杰,郁梅,蒋刚毅.用于立体视频会议系统的立体对象分割与跟踪算法.计算机辅助设计与图形学学报,2004,16(3):307-312.
[37]安平,刘苏醒,高欣,张兆杨.基于视差和阈值分割的立体视频对象提取.中国图象图形学报,2006,11(11):1669-1672.
[38]N.Thakoor,J.Gao,X.Jean.Automatic object detection in video sequences with camera in motion.International Journal of Image and Graphics,2008,8(4):573-600.
[39]张倩,张兆杨,安平.基于压缩域信息的立体视频对象分割方法.中国图象图形学报.2008,13(10):2047-2050.
[40]J.Wang,P.Bhat,R.Colburn,M.Agrawala,M.Cohen.Interactive video cutout.ACM SIGGRAPH 2005,585-594.
[41]C.Rother,V.Kolmogorov,A.Blake."GrabCut":interactive foreground extraction using iterated graph cuts.ACM Transactions on Graphics(TOG),2004,23(3):309-314.
[42]赵明,陈纯,邬正平.基于层次光流的半自动时空视频分割技术.中国图象图形学报,2002,7(8):759-764.
[43]M.Kim,J.Jeon,J.Kwak,M.Lee,C.Ahn.Moving object segmentation in video sequences by user interaction and automatic object tracking.Image and Vision Computing,2001,19(5):245-260.
[44]S.Liu,C.Fu,S.Chang.Statistical change detection with moments under time-varyingillumination.IEEE Transactions on Image Processing,1998,7(9):1258-1268.
[45]R.Mech,M.Wollborn.A noise robust method for 2D shape estimation of moving objects in video sequences considering a moving camera.Signal Processing,1998,66(2):203-217.
[46]孙瑾,顾宏斌,郑吉平.一种基于梯度方向信息的运动目标检测算法.中国图象图形学报,2008,13(3):571-579.
[47]Vishal Jain,Benjamin B.Kimia,J.L.Mundy.Background modeling based on subpixel edges.IEEE Conference on Image Processing,2007,321-324.
[48]M.Heikkila,M.Pietikainen.A texture-based method for modeling the background and detecting moving objects.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):657-662.
[49]T.Ojala,M.Pietikainen,and T.Maenpaa,Multiresolution gray scale and rotation invariant texture analysis with local binary patterns.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[50]徐剑,丁晓青,王生进,吴佑寿.一种融合局部纹理和颜色信息的背景减除方法.自动化学报,2009,35(9):1145-1150.
[51]邓宇,李振波,李华.图切割支持的融合颜色和梯度特征的实时背景减除方法.计算机辅助设计与图形学学报,2006,18(11):1741-1747.
[52]J.Yao,J.Odobez.Multi-layer background subtraction based on color and texture.CVPR Workshop on Visual Surveillance,2007:1-8.
[53]M.Mason,Z.Duric,Using histograms to detect and track objects in color video,in Applied Imagery Pattern Recognition Workshop,2001:154-159.
[54]Y.Chen,C.Chen,C.Huang,Y.Hung.Efficient hierarchical method for background subtraction.Pattern Recognition 2007,40(10):2706-2715.
[55]张彦,周忠,吴威.一种用于运动物体检测的自适应更新背景模型.计算机辅助设计与图形学学报,2008,20(10):1316-1324.
[56]J.Xiao,M.Shah.Motion layer extraction in the presence of occlusion using graph cuts.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005:1644-1659.
[57]T.Parag,A.Elgammal,A.Mittal.A framework for feature selection for background subtraction.IEEE Conference on Computer Vision and Pattern Recognition 2006,1916-1923.
[58]N.Friedman,S.Russell.Image segmentation in video sequences:A probabilistic approach.International Conference on Uncertainty in Artificial Intelligence,1996,175-181.
[59]P.KaewTraKulPong,R.Bowden.An improved adaptive background mixture model for real-time tracking with shadow detection.European Workshop on Advanced Video Based Surveillance Systems.2001,149-158.
[60]Z.Zivkovic.Improved adaptive Gaussian mixture model for background subtraction.International Conference on Pattern Recognition,2004,28-31.
[61]A.Mittal,N.Paragios.Motion-based background subtraction using adaptive kernel density estimation.IEEE Conference on Computer Vision and Pattern Recognition,2004,302-309.
[62]顾建栋,刘志,张兆杨.结合核密度估计和边缘信息的运动对象分割算法.计算机辅助设计与图形学学报,2009(002):223-228.
[63]彭铁根,黄宴委,吴惕华.自适应带宽中值偏移视频图像分割研究.系统仿真学报,2005,17(9):2115-2117.
[64]J.Wang,B.Thiesson,Y.Xu,M.Cohen.Image and video segmentation by anisotropic kernel mean shift.Lecture Notes in Computer Science,2004:238-249.
[65]M.Piccurdi,Z.Jan.Mean-shift background image modeling.IEEE Conference on Image Processing,2004,3399-3402.
[66]Y.Liu,H.Yao,W.Gao,X.Chen,D.Zhao.Nonparametric background generation.Journal of Visual Communication and Image Representation,2007,18(3):253-263.
[67]B.Han,D.Comaniciu,L.Davis.Sequential kernel density approximation through mode propagation:applications to background modeling.Asian Conference on Computer Vision,2004,1186-1197.
[68]Y.Ke,R.Sukthankar,M.Hebert.Efficient temporal mean shift for activity recognition in video.International Conference on Neural Information Processing Systems, 2005, 78-80.
[69] K. Kim, T. Chalidabhongse, D. Harwood, L. Davis. Real-time foreground-background segmentation using codebook model. Real-Time Imaging, 2005, 11(3): 172-185.
[70] A. Baumann, M. Boltz, J. Ebling, M. Koenig, H. Loos, et al. A review and comparison of measures for automatic video surveillance systems. EURASIP Journal on Image and Video Processing, 2008, 2008: 1-30.
[71] F. Oberti, E. Stringa, G Vernazza. Performance evaluation criterion for characterizing video-surveillance systems. Real-Time Imaging, 2001, 7(5):457-471.
[72] F. Bashir, F. Porikli. Performance evaluation of object detection and tracking systems. IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2006, 7-14
[73] S. Muller-Schneiders, T. Jager, H. Loos, W. Niem. Performance evaluation of a real time video surveillance system. IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005,137-143.
[74] V. Manohar, P. Soundararajan, H. Raju, D. Goldgof, R. Kasturi, et al.Performance evaluation of object detection and tracking in video, in Asian Conference on Computer Vision, 2006: 151-155.
[75] V. Mariano, J. Min, J. Park, R. Kasturi, D. Mihalcik, et al. Performance evaluation of object detection algorithms. International Conference on Pattern Recognition, 2002, 965-969.
[76] K. Fukunaga, L. Hostetler. The estimation of the gradient of a density function,with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32-40.
[77] D. Comaniciu, P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 2002: 603-619.
[78]J.Park,A.Tabb,A.Kak.Hierarchical data structure for real-time background subtraction.IEEE Conference on Image Processing,2006:1849-1852.
[79]G.Dalley,J.Migdal,W.Grimson.Background subtraction for temporally irregular dynamic textures.IEEE Workshop on Applications of Computer Vision,2008,1-7.
[80]J.Sun,W.Zhang,X.Tang,H.Shum.Background cut.Europe Conference on Computer Vision,2006,628-641.
[81]P.Jodoin,M.Mignotte,J.Konrad.Statistical background subtraction using spatial cues.IEEE Transactions on Circuits and Systems for Video Technology,2007,17(12):1758-1763.
[82]褚一平,叶修梓,张引,张三元.基于分层MRF模型的抗抖动视频分割算法.浙江大学学报(工学版),2007,41(11):1793-1796.
[83]K.Patwardhan,G.Sapiro,V.Morellas.Robust foreground detection in video using pixel layers.IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(4):746-751.
[84]K.Zhang,M.Tang,J.Kwok.Applying neighborhood consistency for fast clustering and kernel density estimation.IEEE Conference on Computer Vision and Pattern Recognition,2005,1001-1007.
[85]Y.Sun,B.Yuan,Z.Miao,C.Wan.Better foreground segmentation for static cameras via new energy form and dynamic graph-cut.International Conference on Pattern Recognition,2006,49-52.
[86]V.Kolmogorov,A.Criminisi,A.Blake,G.Cross,C.Rother.Probabilistic fusion of stereo with color and contrast for bi-layer segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006.
[87]A.Criminisi,G.Cross,A.Blake,V.Kolmogorov.Bilayer segmentation of live video.IEEE Conference on Computer Vision and Pattern Recognition,2006,53-60.
[88]J.Besag.Spatial interaction and the statistical analysis of lattice systems.Journal of the Royal Statistical Society.Series B(Methodological),1974:192-236.
[89]Y.Boykov,O.Veksler,R.Zabih.Fast approximate energy minimization via graph cuts.IEEE Transactions on Pattern Analysis and Machine Intelligence,2001:1222-1239.
[90]N.Howe,A.Deschamps.Better foreground segmentation through graph cuts.http://arxiv.org/abs/cs.CV/0401017.
[91]M.Green.The appropriate and effective use of security technologies in US Schools.A Guide for Schools and Law Enforcement Agencies.1999.
[92]R.Collins,A.Lipton,T.Kanade,H.Fujiyoshi,D.Duggins,et al.A system for video surveillance and monitoring:VSAM final report.Robotics Inst.,CMU-RI-TR-00-12,2000.
[93]I.Haritaoglu,D.Harwood,L.Davis.W4:Real-time surveillance of people and their activities.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):809-830.
[94]A.Hampapur,L.Brown,J.Connell,M.Lu,H.Merkl,et al.The IBM smart surveillance system.IEEE Conference on Computer Vision and Pattern Recognition,2004,59-62.
[95]N.Siebel,S.Maybank.The advisor visual surveillance system.ECCV Workshop on Applications of Computer Vision 2004,103-111.
[96]王素玉,沈兰荪.智能视觉监控技术研究进展.中国图象图形学报,2007,12(9):1505-1514.
[97]W.Hu,M.Hu,X.Zhou,T.Tan,J.Lou,et al.Principal axis-based correspondence between multiple cameras for people tracking.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):663-671.
[98]T.Li,S.Li.Real-time multiple objects tracking with occlusion handling in dynamic scenes.IEEE Conference on Computer Vision and Pattern Recognition 2005, 970- 975.
[99] H. Lombaert, Y. Sun, L. Grady, C. Xu. A multilevel banded graph cuts method for fast image segmentation. IEEE International Conference on Computer Vision.2005, 259 - 265.
[2]T.Ebrahimi,C.Home.MPEG-4 natural video coding-An overview.Signal Processing:Image Communication,2000,15(4-5):365-385.
[3]S.Chang,T.Sikora,A.Purl.Overview of the MPEG-7 standard.IEEE Transactions on Circuits and Systems for Video Technology,2001,11(6):688-695.
[4]孔晓东.智能视频监控技术研究.上海交通大学博士学位论文,2008.
[5]J.Aguilera,D.Thirde,M.Kampel,M.Borg,G.Fernandez,et al.Visual surveillance for airport monitoring applications.International Conference on Computer Communications and Networks,2005,255-262.
[6]D.M.Tsai,S.Lai.Independent component analysis-based background subtraction for indoor surveillance.IEEE Transactions on Image Processing,2009,18:158-167.
[7]W.Hu,X.Xiao,D.Xie,T.Tan,S.Maybank.Traffic accident prediction using 3-D model-based vehicle tracking.IEEE Transactions on Vehicular Technology,2004,53(3):677-694.
[8]W.Hu,T.Tan,L.Wang,S.Maybank.A survey on visual surveillance of object motion and behaviors.IEEE Transactions on Systems,Man and Cybernetics,2004,34(3):334-352.
[9]朱杰杰.增强现实中多视图几何问题的研究.浙江大学博士学位论文,2007.
[10]童晓峰,刘青山,卢汉清.体育视频分析.计算机学报,2008,31(7):1242-1251.
[11]吴思,张勇东,林守勋,李豪杰.动态场景视频序列中的前景区域自动提取.计算机辅助设计与图形学学报,2005,17(2):359-363.
[12]王嘉,王海峰,刘青山,卢汉清.基于三参数模型的快速全局运动估计.计 算机学报,2006,29(6):920-927.
[13]郭丽,龚声蓉,崔志明.一种快速全局运动估计算法.中国图象图形学报,2007,12(12):2119-2126.
[14]F.Dufaux,J.Konrad.Efficient,robust,and fast global motion estimation for video coding.IEEE Transactions on Image Processing,2000,9(3):497-501.
[15]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,25(3):225-237.
[16]A.J.Lipton,H.Fujiyoshi,R.Patil.Moving target classification and tracking from real-time video.IEEE Workshop on Applications of Computer Vision,1998,8-14.
[17]杨莉,张弘,李玉山.视频运动对象的自动分割.计算机辅助设计与图形学学报,2004,16(3):301-306.
[18]刘鑫,刘辉,强振平,耿续涛.混合高斯模型和帧间差分相融合的自适应背景模型.中国图象图形学报,2008,13(4):729-734.
[19]J.L.Barron,D.J.Fleet,S.Beauchemin.Performance of optical flow techniques.International Journal of Computer Vision,1994,12(1):43-47.
[20]杨威,张田文.复杂景物环境下运动目标检测的新方法.计算机研究与发展,1998,35(8):724-728.
[21]C.R.Wren,A.Azarbayejani,T.Darrell,A.Pentland.Pfinder:Real-time tracking of the human body.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):780-785.
[22]C.Stauffer,W.Grimson.Adaptive background mixture models for real-time tracking.IEEE Conference on Computer Vision and Pattern Recognition 1999,246-252.
[23]T.Matsuyama,T.Ohya,H.Habe.Background subtraction for non-stationary scenes.Asian Conference on Computer Vision,2000,662-667.
[24]A.Elgammal,R.Duraiswami,L.S.Davis.Efficient non-parametric adaptive color modeling using fast gauss transform.IEEE Conference on Computer Vision and Pattern Recognition,2001,563-570.
[25]A.Elgammal,R.Duraiswami,D.Harwood,L.S.Davis.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance.Proceedings of the IEEE,2002,90(7):1151-1163.
[26]O.Javed,K.Shafique,M.Shah.A hierarchical approach to robust background subtraction using color and gradient information.IEEE Workshop on Motion and Video Computing,2002,22-27.
[27]R.Cucchiara,C.Grana,M.Piccardi,A.Prati.Detecting moving objects,ghosts,and shadows in video streams.IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,10(8):1337-1342.
[28]D.Lee.Effective Gaussian Mixture Learning for Video Background Subtraction.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27:827-832.
[29]Q.Zhu,S.Avidan,K.Cheng.Learning a sparse,comer-based representation for background modelling.International Conference on Computer Vision,2005,678-685.
[30]L.Li,W.Huang,Y.Gu,Q.Tian.Statistical modeling of complex backgrounds for foreground object detection.IEEE Transactions on Image Processing,2004,13(11):1459-1472.
[31]R.Babu,K.Ramakrishnan,S.Srinivasan.Video object segmentation:a compressed domain approach.IEEE Transactions on Circuits and Systems for Video Technology,2004,14(4):462-474.
[32]孙涛,杨高波,刘理,张兆扬.一种基于梯度模型的MPEG压缩域的运动对象分割算法.中国图象图形学报,2008,13(6):1109-1114.
[33]W.Zeng,J.Du,W.Gao,Q.Huang.Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model.Real-Time Imaging,2005,11(4):290-299.
[34]杨高波,张兆扬.MPEG-4框架下的视频对象分割及其关键技术分析.通信 学报,2007,28(10):109-122.
[35]高欣,安平,刘佳,张兆扬.基于视差和变化检测的立体视频对象分割.上海大学学报:自然科学版,2006,12(2):116-119.
[36]朱仲杰,郁梅,蒋刚毅.用于立体视频会议系统的立体对象分割与跟踪算法.计算机辅助设计与图形学学报,2004,16(3):307-312.
[37]安平,刘苏醒,高欣,张兆杨.基于视差和阈值分割的立体视频对象提取.中国图象图形学报,2006,11(11):1669-1672.
[38]N.Thakoor,J.Gao,X.Jean.Automatic object detection in video sequences with camera in motion.International Journal of Image and Graphics,2008,8(4):573-600.
[39]张倩,张兆杨,安平.基于压缩域信息的立体视频对象分割方法.中国图象图形学报.2008,13(10):2047-2050.
[40]J.Wang,P.Bhat,R.Colburn,M.Agrawala,M.Cohen.Interactive video cutout.ACM SIGGRAPH 2005,585-594.
[41]C.Rother,V.Kolmogorov,A.Blake."GrabCut":interactive foreground extraction using iterated graph cuts.ACM Transactions on Graphics(TOG),2004,23(3):309-314.
[42]赵明,陈纯,邬正平.基于层次光流的半自动时空视频分割技术.中国图象图形学报,2002,7(8):759-764.
[43]M.Kim,J.Jeon,J.Kwak,M.Lee,C.Ahn.Moving object segmentation in video sequences by user interaction and automatic object tracking.Image and Vision Computing,2001,19(5):245-260.
[44]S.Liu,C.Fu,S.Chang.Statistical change detection with moments under time-varyingillumination.IEEE Transactions on Image Processing,1998,7(9):1258-1268.
[45]R.Mech,M.Wollborn.A noise robust method for 2D shape estimation of moving objects in video sequences considering a moving camera.Signal Processing,1998,66(2):203-217.
[46]孙瑾,顾宏斌,郑吉平.一种基于梯度方向信息的运动目标检测算法.中国图象图形学报,2008,13(3):571-579.
[47]Vishal Jain,Benjamin B.Kimia,J.L.Mundy.Background modeling based on subpixel edges.IEEE Conference on Image Processing,2007,321-324.
[48]M.Heikkila,M.Pietikainen.A texture-based method for modeling the background and detecting moving objects.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):657-662.
[49]T.Ojala,M.Pietikainen,and T.Maenpaa,Multiresolution gray scale and rotation invariant texture analysis with local binary patterns.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[50]徐剑,丁晓青,王生进,吴佑寿.一种融合局部纹理和颜色信息的背景减除方法.自动化学报,2009,35(9):1145-1150.
[51]邓宇,李振波,李华.图切割支持的融合颜色和梯度特征的实时背景减除方法.计算机辅助设计与图形学学报,2006,18(11):1741-1747.
[52]J.Yao,J.Odobez.Multi-layer background subtraction based on color and texture.CVPR Workshop on Visual Surveillance,2007:1-8.
[53]M.Mason,Z.Duric,Using histograms to detect and track objects in color video,in Applied Imagery Pattern Recognition Workshop,2001:154-159.
[54]Y.Chen,C.Chen,C.Huang,Y.Hung.Efficient hierarchical method for background subtraction.Pattern Recognition 2007,40(10):2706-2715.
[55]张彦,周忠,吴威.一种用于运动物体检测的自适应更新背景模型.计算机辅助设计与图形学学报,2008,20(10):1316-1324.
[56]J.Xiao,M.Shah.Motion layer extraction in the presence of occlusion using graph cuts.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005:1644-1659.
[57]T.Parag,A.Elgammal,A.Mittal.A framework for feature selection for background subtraction.IEEE Conference on Computer Vision and Pattern Recognition 2006,1916-1923.
[58]N.Friedman,S.Russell.Image segmentation in video sequences:A probabilistic approach.International Conference on Uncertainty in Artificial Intelligence,1996,175-181.
[59]P.KaewTraKulPong,R.Bowden.An improved adaptive background mixture model for real-time tracking with shadow detection.European Workshop on Advanced Video Based Surveillance Systems.2001,149-158.
[60]Z.Zivkovic.Improved adaptive Gaussian mixture model for background subtraction.International Conference on Pattern Recognition,2004,28-31.
[61]A.Mittal,N.Paragios.Motion-based background subtraction using adaptive kernel density estimation.IEEE Conference on Computer Vision and Pattern Recognition,2004,302-309.
[62]顾建栋,刘志,张兆杨.结合核密度估计和边缘信息的运动对象分割算法.计算机辅助设计与图形学学报,2009(002):223-228.
[63]彭铁根,黄宴委,吴惕华.自适应带宽中值偏移视频图像分割研究.系统仿真学报,2005,17(9):2115-2117.
[64]J.Wang,B.Thiesson,Y.Xu,M.Cohen.Image and video segmentation by anisotropic kernel mean shift.Lecture Notes in Computer Science,2004:238-249.
[65]M.Piccurdi,Z.Jan.Mean-shift background image modeling.IEEE Conference on Image Processing,2004,3399-3402.
[66]Y.Liu,H.Yao,W.Gao,X.Chen,D.Zhao.Nonparametric background generation.Journal of Visual Communication and Image Representation,2007,18(3):253-263.
[67]B.Han,D.Comaniciu,L.Davis.Sequential kernel density approximation through mode propagation:applications to background modeling.Asian Conference on Computer Vision,2004,1186-1197.
[68]Y.Ke,R.Sukthankar,M.Hebert.Efficient temporal mean shift for activity recognition in video.International Conference on Neural Information Processing Systems, 2005, 78-80.
[69] K. Kim, T. Chalidabhongse, D. Harwood, L. Davis. Real-time foreground-background segmentation using codebook model. Real-Time Imaging, 2005, 11(3): 172-185.
[70] A. Baumann, M. Boltz, J. Ebling, M. Koenig, H. Loos, et al. A review and comparison of measures for automatic video surveillance systems. EURASIP Journal on Image and Video Processing, 2008, 2008: 1-30.
[71] F. Oberti, E. Stringa, G Vernazza. Performance evaluation criterion for characterizing video-surveillance systems. Real-Time Imaging, 2001, 7(5):457-471.
[72] F. Bashir, F. Porikli. Performance evaluation of object detection and tracking systems. IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2006, 7-14
[73] S. Muller-Schneiders, T. Jager, H. Loos, W. Niem. Performance evaluation of a real time video surveillance system. IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005,137-143.
[74] V. Manohar, P. Soundararajan, H. Raju, D. Goldgof, R. Kasturi, et al.Performance evaluation of object detection and tracking in video, in Asian Conference on Computer Vision, 2006: 151-155.
[75] V. Mariano, J. Min, J. Park, R. Kasturi, D. Mihalcik, et al. Performance evaluation of object detection algorithms. International Conference on Pattern Recognition, 2002, 965-969.
[76] K. Fukunaga, L. Hostetler. The estimation of the gradient of a density function,with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32-40.
[77] D. Comaniciu, P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 2002: 603-619.
[78]J.Park,A.Tabb,A.Kak.Hierarchical data structure for real-time background subtraction.IEEE Conference on Image Processing,2006:1849-1852.
[79]G.Dalley,J.Migdal,W.Grimson.Background subtraction for temporally irregular dynamic textures.IEEE Workshop on Applications of Computer Vision,2008,1-7.
[80]J.Sun,W.Zhang,X.Tang,H.Shum.Background cut.Europe Conference on Computer Vision,2006,628-641.
[81]P.Jodoin,M.Mignotte,J.Konrad.Statistical background subtraction using spatial cues.IEEE Transactions on Circuits and Systems for Video Technology,2007,17(12):1758-1763.
[82]褚一平,叶修梓,张引,张三元.基于分层MRF模型的抗抖动视频分割算法.浙江大学学报(工学版),2007,41(11):1793-1796.
[83]K.Patwardhan,G.Sapiro,V.Morellas.Robust foreground detection in video using pixel layers.IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(4):746-751.
[84]K.Zhang,M.Tang,J.Kwok.Applying neighborhood consistency for fast clustering and kernel density estimation.IEEE Conference on Computer Vision and Pattern Recognition,2005,1001-1007.
[85]Y.Sun,B.Yuan,Z.Miao,C.Wan.Better foreground segmentation for static cameras via new energy form and dynamic graph-cut.International Conference on Pattern Recognition,2006,49-52.
[86]V.Kolmogorov,A.Criminisi,A.Blake,G.Cross,C.Rother.Probabilistic fusion of stereo with color and contrast for bi-layer segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006.
[87]A.Criminisi,G.Cross,A.Blake,V.Kolmogorov.Bilayer segmentation of live video.IEEE Conference on Computer Vision and Pattern Recognition,2006,53-60.
[88]J.Besag.Spatial interaction and the statistical analysis of lattice systems.Journal of the Royal Statistical Society.Series B(Methodological),1974:192-236.
[89]Y.Boykov,O.Veksler,R.Zabih.Fast approximate energy minimization via graph cuts.IEEE Transactions on Pattern Analysis and Machine Intelligence,2001:1222-1239.
[90]N.Howe,A.Deschamps.Better foreground segmentation through graph cuts.http://arxiv.org/abs/cs.CV/0401017.
[91]M.Green.The appropriate and effective use of security technologies in US Schools.A Guide for Schools and Law Enforcement Agencies.1999.
[92]R.Collins,A.Lipton,T.Kanade,H.Fujiyoshi,D.Duggins,et al.A system for video surveillance and monitoring:VSAM final report.Robotics Inst.,CMU-RI-TR-00-12,2000.
[93]I.Haritaoglu,D.Harwood,L.Davis.W4:Real-time surveillance of people and their activities.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):809-830.
[94]A.Hampapur,L.Brown,J.Connell,M.Lu,H.Merkl,et al.The IBM smart surveillance system.IEEE Conference on Computer Vision and Pattern Recognition,2004,59-62.
[95]N.Siebel,S.Maybank.The advisor visual surveillance system.ECCV Workshop on Applications of Computer Vision 2004,103-111.
[96]王素玉,沈兰荪.智能视觉监控技术研究进展.中国图象图形学报,2007,12(9):1505-1514.
[97]W.Hu,M.Hu,X.Zhou,T.Tan,J.Lou,et al.Principal axis-based correspondence between multiple cameras for people tracking.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):663-671.
[98]T.Li,S.Li.Real-time multiple objects tracking with occlusion handling in dynamic scenes.IEEE Conference on Computer Vision and Pattern Recognition 2005, 970- 975.
[99] H. Lombaert, Y. Sun, L. Grady, C. Xu. A multilevel banded graph cuts method for fast image segmentation. IEEE International Conference on Computer Vision.2005, 259 - 265.