基于多视点的三维姿态运动重建与跟踪
|
摘要
人体运动姿态捕获与姿态分析是计算机视觉领域研究的重点问题之一,在影视游戏、监控分析、人机交互、虚拟现实、医疗诊断和运动分析等方面具有重要的应用价值,具有广阔的应用前景。当前,由于存在非刚性的人体形状、二维到三维投影的歧义性、自遮挡、高维参数的恢复、真实场景下图像特征提取与匹配等诸多困难,从数字视频图像中恢复出人体三维运动姿态存在大量的理论问题。因此,研究人体姿态捕获与姿态分析既具有理论研究意义,又具有工程应用价值。
人体姿态重建与跟踪是人体姿态分析的基础之一。本文围绕人体姿态重建及跟踪开展研究,包括人体与手势的重建与跟踪,研究了在多相机环绕环境下的人体定位和姿态跟踪算法和方法,可以为面向自然真实场景的人体姿态捕获与姿态分析提供理论基础和系统框架。具体地,本文的主要研究内容和创新点如下：
第一、针对复杂场景下的前景目标提取,提出了一种结合深度信息和肤色信息的分割方法。在具有复杂运动背景的环境中提取前景目标时,利用深度信息可以帮助避免复杂背景和背景物体运动的干扰。本文把RGB相机和Depth相机二者相机结合,提出一种把深度信息和肤色信息结合的分割方法,深度信息可以帮助避免复杂背景的干扰。在背景复杂或有干扰的情况下,提取的前景效果相对于传统方法来说更为健壮有效。
第二、针对姿态构造的特点,结合层次策略,提出了一种间接人体模型的自动初始化方法。传统方法通常使用学习方法或已知参数的固定姿态,以及形状控制参数、层次策略等方法构造肢体骨架。本文结合层次构造法,提出了基于矢量合成分析方法和改进细化体素方法,构建了具有与真人匹配的人体骨架和拓扑结构,以自适应的模型来替代普遍模型。
第三、针对一个旋转角分解为分别绕XYZ轴的旋转角时,传统方法是必须假设其中一个旋转角度为零,本文提出了一种引入构造分解法求解旋转自由度的方法。一个空间点相对于某点的旋转角,如果要分解为相对于XYZ三轴的旋转角度,通常需要计算出绕XYZ三轴的旋转分量,传统方法必须假设其中一个旋转角度为零,通过降维来求解另外两个旋转分量,该过程不能真实描述各关节点的运动情况。为了解决此问题,本文引入构造分解法,R是正交矩阵,存在U和Q,使得正交阵R=QUT,选取适当矩阵元素,可使正交矩阵R分解为分别绕Z轴、X轴和Y轴的旋转矩阵的乘积,从而有效地表达了各关节点的旋转信息。
第四、针对传统运动模型自由度较高的问题,提出了新的运动模型,降低了自由度维数,并利用此模型实现了运动姿态跟踪。合理利用人体生理运动原则及非标记运动的特点,对运动模型重新设计,将运动自由度维数降至最低,使得自由度的求解速度加快。提出使用一种运动囊概念,运动囊反向驱动骨架的节点,节点必须在位移和角度上跟随变化,通过使用多线索跟踪技术,把运动囊轮廓边界线索和重合度线索结合,用梯度法计算姿态自由度近似最优值,进而更新人体骨架各节点的姿态参数。
第五、基于前面提出的算法,开发出了一套新的基于姿态捕获的三维重建原型系统。设计了一维标定物对相机内外参标定,标定方法快捷实用。对前面各章方案的有效性进行分析和验证。系统简单实用、成本较低。
The capture of human body posture and its posture analysis is one of the hottest research topics in the field of computer vision. It has great potential in various applications such as movies&games, surveillance analysis, human machine interaction, virtual reality, medical diagnosis and motion analysis. However, there are still many difficulties such as the non-rigid body shape, the ambiguity of2D to3D projection, self-occlusion, recovery of high-dimensional parameters and the feature extraction and matching under real situations. There are still many theoretic issues about the recovery of human body posture from digital video. Therefore, the research on human body posture analysis is of both theoretic and practical value.
Reconstruction and analysis of human body posture is one of the bases of human body posture analysis. This thesis focuses on human body posture analysis, including the reconstruction and tracking of human body and hand posture. It researches on the human body location and posture tracking under multi-camera environment, which provides theoretic basis and systematic framework for human body posture capturing and analysis. In detail, the main work and contributions are summarized as follows.
First, a novel algorithm which combines depth and skin information and can extract fore-ground target object in the complex scene is proposed. To segment the fore-ground image in the complex scene, the depth segmentation method is an effective method to avoid the interference in the complex scene and moving objects in the background. With the RGB camera joint with Depth camera, a segmentation method combined with depth information and skin color information is proposed. The depth information help to get clear fore-ground target object. The method is robust and efficient even in the background there have the complex scene and moving object.
Second, according to the characters of pose structure, and combining the hierarchy strategy, an auto-initialized algorithm which used in indirect model human model is proposed. The general methods retrieving the pose parameters are matching a fixed pose prepared beforehand which has known the parameter, or using the learning method, or shape controlling parameters, or hierarchical strategy. We combine hierarchical strategy to propose Vector Synthesis Analysis (VSA) method and improve the voxel thinning method. The auto-initialized algorithm builds the human skeleton and topology which is matching the real man. We construct adaptive model method instead of general method.
Third, when a rotation angle is decomposition three sub-angles around XYZ axes respectively, the traditional methods is to make a hypothesis that one of the sub-angles must be zero. A construction of decomposition method which is used in solving the problem is proposed in the paper. If a rotation angle that a point rotates around another point need to be decomposed into three sub-angles around axes XYZ respectively, the general method is to reduce the number of dimensions that make a hypothesis that one of the sub-angles is zero, and then solve the other two rotation sub-angles. The process is not exactly describing the motion of the joints. In order to solve the problem, we introduce decomposition method. The rotation matrix R is orthogonal matrix. There are existing matrixes U and Q to make R=QUT Selecting the appropriate matrix elements, the decomposition method can decompose arbitrary rotation angle into combination of three sub-angles rotating around XYZ axes. The method effectively represents the rotation information of each joint.
Fourth, according to traditional motion model that exist the problem of high dimension of Degree of Freedom (DOF), a new motion model which has the lower dimension of DOF is proposed, and we use it to implement the posture parameters recovery. Rationally using the physiological motion principles and the characters of markless motion, we redesign the human motion model and decrease the quantity of DOF to lower, which make help accelerating to recover the pose parameters. And we also use a motion capsule concept. The motion capsule inversely drives the joints of skeleton which have to change with translation and rotation angle. We use multi-cue tracking technology which combined with silhouette cue and overlap cue of motion capsule. We used local gradient-based method to recover the optimal parameters and update the pose with them.
Fifth, a framework of markless human posture capture system is constructed based on above-mentioned work. We designed a1D calibration rod to calibrate the intrinsic and extrinsic parameters which make calibration easier and quicker. And we construct a simple, practical, low-cost prototype system at last.
人体姿态重建与跟踪是人体姿态分析的基础之一。本文围绕人体姿态重建及跟踪开展研究,包括人体与手势的重建与跟踪,研究了在多相机环绕环境下的人体定位和姿态跟踪算法和方法,可以为面向自然真实场景的人体姿态捕获与姿态分析提供理论基础和系统框架。具体地,本文的主要研究内容和创新点如下：
第一、针对复杂场景下的前景目标提取,提出了一种结合深度信息和肤色信息的分割方法。在具有复杂运动背景的环境中提取前景目标时,利用深度信息可以帮助避免复杂背景和背景物体运动的干扰。本文把RGB相机和Depth相机二者相机结合,提出一种把深度信息和肤色信息结合的分割方法,深度信息可以帮助避免复杂背景的干扰。在背景复杂或有干扰的情况下,提取的前景效果相对于传统方法来说更为健壮有效。
第二、针对姿态构造的特点,结合层次策略,提出了一种间接人体模型的自动初始化方法。传统方法通常使用学习方法或已知参数的固定姿态,以及形状控制参数、层次策略等方法构造肢体骨架。本文结合层次构造法,提出了基于矢量合成分析方法和改进细化体素方法,构建了具有与真人匹配的人体骨架和拓扑结构,以自适应的模型来替代普遍模型。
第三、针对一个旋转角分解为分别绕XYZ轴的旋转角时,传统方法是必须假设其中一个旋转角度为零,本文提出了一种引入构造分解法求解旋转自由度的方法。一个空间点相对于某点的旋转角,如果要分解为相对于XYZ三轴的旋转角度,通常需要计算出绕XYZ三轴的旋转分量,传统方法必须假设其中一个旋转角度为零,通过降维来求解另外两个旋转分量,该过程不能真实描述各关节点的运动情况。为了解决此问题,本文引入构造分解法,R是正交矩阵,存在U和Q,使得正交阵R=QUT,选取适当矩阵元素,可使正交矩阵R分解为分别绕Z轴、X轴和Y轴的旋转矩阵的乘积,从而有效地表达了各关节点的旋转信息。
第四、针对传统运动模型自由度较高的问题,提出了新的运动模型,降低了自由度维数,并利用此模型实现了运动姿态跟踪。合理利用人体生理运动原则及非标记运动的特点,对运动模型重新设计,将运动自由度维数降至最低,使得自由度的求解速度加快。提出使用一种运动囊概念,运动囊反向驱动骨架的节点,节点必须在位移和角度上跟随变化,通过使用多线索跟踪技术,把运动囊轮廓边界线索和重合度线索结合,用梯度法计算姿态自由度近似最优值,进而更新人体骨架各节点的姿态参数。
第五、基于前面提出的算法,开发出了一套新的基于姿态捕获的三维重建原型系统。设计了一维标定物对相机内外参标定,标定方法快捷实用。对前面各章方案的有效性进行分析和验证。系统简单实用、成本较低。
The capture of human body posture and its posture analysis is one of the hottest research topics in the field of computer vision. It has great potential in various applications such as movies&games, surveillance analysis, human machine interaction, virtual reality, medical diagnosis and motion analysis. However, there are still many difficulties such as the non-rigid body shape, the ambiguity of2D to3D projection, self-occlusion, recovery of high-dimensional parameters and the feature extraction and matching under real situations. There are still many theoretic issues about the recovery of human body posture from digital video. Therefore, the research on human body posture analysis is of both theoretic and practical value.
Reconstruction and analysis of human body posture is one of the bases of human body posture analysis. This thesis focuses on human body posture analysis, including the reconstruction and tracking of human body and hand posture. It researches on the human body location and posture tracking under multi-camera environment, which provides theoretic basis and systematic framework for human body posture capturing and analysis. In detail, the main work and contributions are summarized as follows.
First, a novel algorithm which combines depth and skin information and can extract fore-ground target object in the complex scene is proposed. To segment the fore-ground image in the complex scene, the depth segmentation method is an effective method to avoid the interference in the complex scene and moving objects in the background. With the RGB camera joint with Depth camera, a segmentation method combined with depth information and skin color information is proposed. The depth information help to get clear fore-ground target object. The method is robust and efficient even in the background there have the complex scene and moving object.
Second, according to the characters of pose structure, and combining the hierarchy strategy, an auto-initialized algorithm which used in indirect model human model is proposed. The general methods retrieving the pose parameters are matching a fixed pose prepared beforehand which has known the parameter, or using the learning method, or shape controlling parameters, or hierarchical strategy. We combine hierarchical strategy to propose Vector Synthesis Analysis (VSA) method and improve the voxel thinning method. The auto-initialized algorithm builds the human skeleton and topology which is matching the real man. We construct adaptive model method instead of general method.
Third, when a rotation angle is decomposition three sub-angles around XYZ axes respectively, the traditional methods is to make a hypothesis that one of the sub-angles must be zero. A construction of decomposition method which is used in solving the problem is proposed in the paper. If a rotation angle that a point rotates around another point need to be decomposed into three sub-angles around axes XYZ respectively, the general method is to reduce the number of dimensions that make a hypothesis that one of the sub-angles is zero, and then solve the other two rotation sub-angles. The process is not exactly describing the motion of the joints. In order to solve the problem, we introduce decomposition method. The rotation matrix R is orthogonal matrix. There are existing matrixes U and Q to make R=QUT Selecting the appropriate matrix elements, the decomposition method can decompose arbitrary rotation angle into combination of three sub-angles rotating around XYZ axes. The method effectively represents the rotation information of each joint.
Fourth, according to traditional motion model that exist the problem of high dimension of Degree of Freedom (DOF), a new motion model which has the lower dimension of DOF is proposed, and we use it to implement the posture parameters recovery. Rationally using the physiological motion principles and the characters of markless motion, we redesign the human motion model and decrease the quantity of DOF to lower, which make help accelerating to recover the pose parameters. And we also use a motion capsule concept. The motion capsule inversely drives the joints of skeleton which have to change with translation and rotation angle. We use multi-cue tracking technology which combined with silhouette cue and overlap cue of motion capsule. We used local gradient-based method to recover the optimal parameters and update the pose with them.
Fifth, a framework of markless human posture capture system is constructed based on above-mentioned work. We designed a1D calibration rod to calibrate the intrinsic and extrinsic parameters which make calibration easier and quicker. And we construct a simple, practical, low-cost prototype system at last.
引文
[1]Moeslund T B, Granum E, A survey of computer vision-based human motion capture, Computer Vision and Image Understanding,2001,81 (3):231-268.
[2]Moeslund T B, Hilton A, Kruger V, A survey of advances in vision-based human motion capture and analysis [J]. Computer Vision and Image Understanding, 2006,104 (2),90-126.
[3]Poppe R, Vision-base human motion analysis:An overview [J]. Computer Vision and Image Understanding,2007,108 (1),4-18.
[4]Poppe R, A survey on vision-based human action recognition. Image and Vision Computing.2010,28:976-990.
[5]Weinland D, Ronfard R, Boyer E. A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding.2011,115:224-241.
[6]Loren A S D, Nassir N. Recognizing multiple human activities and tracking full-body pose in unconstrained environments. Pattern Recognition,2012, 45(1):11-23.
[7]Chen C, Yang Y, Nie F P.3D human pose recovery from image by efficient visual feature selection, Computer Vision and Image Understanding.2011, 115(3):290-299.
[8]Maggioni C, Kammerer B, Gesture Computer:history, design and applications, Computer Vision for Human-Machine Interaction. Cambridge University, Press, 1998.
[9]Freeman W, Weissman C, Television control by hand gestures. In:Proc Of Int Conf on Automatic Face and Gesture Recognition, Zurich, Switzerland,1995, 179-183.
[10]Marr D, Vision [M]. W. H. Freeman and Company,1982.
[11]吴朝福.计算机视觉中的数学方法.科学出版社.第一版,2008：94-95.
[12]Wang J, Cohen M F. Image and video matting:a survey. Computer Graphics and Vision.2007,3(2):97-175
[13]Rother C, Kolmogorov V, Blake A. GrabCut-Interactive foreground extraction using iterated graph cuts. In Proc. Siggraph, Los Angeles,2004,309-314.
[14]D.A. Forsythe, M.M. Fleck, Body plans, in:Computer Vision and Pattern Recognition, Puerto Rico,1997,678-683.
[15]Chen C,Zhuang Y T, Xiao J.Silhouette representation and matching for 3D pose discrimination-A comparative study. Image and Vision Computing.2010, 28(4):654-667.
[16]Caillette F. Real-time markerless 3-D human body tracking [dissertation] University of Manchester.2006:54-55.
[17]Dia-Mas L, Munoz-Salinas R, Madrid-Cuevas F J et al. Shape from silhouetteusing Dempster-Shafer theory. Pattern Recognition,2010,43(6): 2119-2131.
[18]Cheung K M, Baker S, Kanade T. Visual hull alignment and refinement across time:A 3D reconstruction Algorithm combining Shape-From-Silhouette with stereo, in:Computer Vision and Pattern Recognition, Madison, Wisconsin, USA,2003,375-382.
[19]Varol A, Shaji A, Salzmann M and P. Fua, Monocular 3D Reconstruction of Locally Textured Surfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(6):1118-1130.
[20]Berclaz J, Fleuret F, Turetken E and Fua P, Multiple Object Tracking using K-Shortest Paths Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33 (9):1806-1819.
[21]Fossati A, Dimitrijevic M, Lepetit V and Fua P, From Canonical Poses to 3-D Motion Capture using a Single Camera, IEEE Transactions on Pattern Analysis and Machine Intelligence,2010.32(7):1165-1181.
[22]Sigal L, Isard M, Haussecker H, et.al. Loose-limbed people:Estimating 3d human pose and motion using non-parametric belief propagation. International Journal of Computer Vision.2012,98:15-48.
[23]Vondrak M, Sigal L, Hodgins J K, et al, Video-based 3D Motion Capture through Biped Control. ACM Transactions on Graphics,2012.31(4):68-79.
[24]Daubney B, Gibson D, Campbell N. Estimating pose of articulated objects using low-level motion. Computer Vision and Image Understanding,2012,116: 330-346.
[25]Iosifidis A, Tefas A, Nikolaidis N, Pitas I. Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis. Computer Vision and Image Understanding,2012,116 (3):347-360.
[26]Yao B P, Khosla A, Li F F. Classifying action and measuring action similarity by modeling the mutual context of objects and human poses. In:Proc Of Int Conf on Machine Learning. Bellevue, WA, USA.2011,232-242.
[27]Zhao X, Fu Y, Ning H Z, Liu Y C, and et al. Human Pose Regression through Multiview Visual Fusion. IEEE Transactions on Circuits and Systems for Video Technology,2010,20(7):957-966.
[28]Yan J C, Song J, Liu Y C:Simultaneous 3D Human Motion Tracking and Voxel Reconstruction. Journal of Optical Engineering,2010,49(9):36-49.
[29]刘国军.基于可移动拍摄大场景下的人体运动跟踪关键技术的研究与应用[dissertation].哈尔滨工业大学,2009：2-21.
[30]Li K, Dai Q H, Xu W L. Markerless Shape and Motion Capture from Multiview Video Sequences. IEEE Transactions on Circuits and Systems for Video Technology,2011,21(3):320-334.
[31]Shen C F, Lin X Y, Shi Y C. Human pose estimation from corrupted silhouettes using a sub-manifold voting strategy in latent variable space. Pattern Recognition Letters,2009,30(4):421-431.
[32]Agarwa A, Triggs B, Recovering 3D human pose from monocular images, IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1):44-58.
[33]Navaratnam R, Fitzgibbon A, Cipolla R. Semi-supervised joint manifold learning for multi-valued regression. In:Proc Of Int Conf on computer vision, Rio de Janerio, Brasil.2007,1-8.
[34]Marcus A B, David J F, Aaron H. Physics-based person tracking using the anthropomorphic walker, International Journal of Computer Vision.2010, 87(1/2):140-155.
[35]Shakhnarovich G, Viola P, Darrell T, Fast pose estimation with parameter-sensitive hashing, in:Proc Of Int Conf on Computer Vision, Nice, France,2003,750-757.
[36]Sminchisescu C, Kanaujia A, Li Z. Discriminative density propagation for 3D human motion estimation. In:computer vision and pattern recognition,San Diego, CA, USA,2005,390-397.
[37]Urtasun R, Darrel T. Sparse probabilistic regression for activity-independent human pose inference. In:computer vision and pattern recognition, Anchorage, Alaska, USA,2008,1-8.
[38]Grauman K, Shakhnarovich G, T. Darrell, Inferring 3D structure with a statistical image-based shape model, in:Proc Of Int Conf on Computer Vision, Nice, France,2003,641-648.
[39]Rosales R, Sclaroff S. Learning body pose via specialized maps. Advances in neural information processing systems.2002,15,1263-1270.
[40]Sigal L, Black M J.Predicting 3D people from 2D pictures. In LNCS:IV Proc of Int Conf on articulated motion and deformable objects, Mallorca, Spain,2006, 185-195.
[41]Ju S, Black M J, & Yacoob, Y. Cardboard people:A parameterized model of articulated motion. In:Automatic Face and Gesture Recognition, Killington, VT, 1996,38-44.
[42]Wang P, Rehg J M. A modular approach to the analysis and evaluation of particle filters for figure tracking. In:computer vision and pattern recognition, New York, NY, USA,2006,790-797.
[43]Deutscher J, Reid I D. Articulated body motion capture by stochastic search. International Journal of Computer Vision, San Diego, CA, USA,2005, 185-205.
[44]Gall J, Rosenhahn B, Seidel H P. Clustered stochastic optimization for object recognition and pose estimation. Pattern Recognition,2007,4713:32-41.
[45]Gall J, Rosenhahn B, Brox T, Seidel H P. Optimization and filtering for human motion capture—A multi-layer framework. International Journal of Computer Vision,2010,87(1):75-92.
[46]Horaud R, Niskanen M, Dewaele G, et al. Human motion tracking by registering an articulated surface to 3-D points and normals. IEEE IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(1):158-163.
[47]John V, Ivekovic S, Trucco E. Articulated human motion tracking with HPSO. In:Proc Of Int Conf on computer vision theory and applications,2009,531-538.
[48]Kehl R, Gool L V. Markerless tracking of complex human motions from multiple views. Computer Vision and Image Understanding.2006, 104(2/3),190-209.
[49]Knossow D, Ronfard R, & Horaud R. Human motion tracking with a kinematic parameterization of extremal contours. International Journal of Computer Vision.2008,79(2):247-269.
[50]Sidenbladh H, Black M J, & Fleet, D.Stochastic tracking of 3D human figures using 2D image motion. In:European Proc Of Int Conf on Computer Vision, Dublin, Ireland,2000,702-718.
[51]MacCormick J, Isard M, Partitioned sampling, articulated objects, and interface-quality hand tracking, in:European Proc Of Int Conf on Computer Vision, Dublin, Ireland,2000,3-19.
[52]Ioffe S, Forsyth D, Human tracking with mixtures of trees, in:Proc Of Int Conf on Computer Vision, Vancouver, Canada,2001,690-695.
[53]Lee C S, Elgammal A. Coupled visual and kinematic manifold models for tracking, International Journal of Computer Vision.2010,87(1-2):118-139.
[54]Zou B J, Chen S, Cao S. Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking. Pattern Recognition,2009,42(7):1559-1571.
[55]Micilotta A, Ong E, Bowden R, Detection and tracking of humans by probabilistic body part assembly, in:Proc Of Int Conf British Machine Vision Conference, Oxford, UK,2005,429-438.
[56]Ramanan D, Forsyth D A, Zisserman A, Strike a pose:tracking people by finding stylized poses, in:Computer Vision and Pattern Recognition, San Diego, California, USA,2005,271-278.
[57]Roberts T J, McKenna S J, Ricketts I W, Human pose estimation using learnt probabilistic region similarities and partial configurations, in:European Proc Of Int Conf on Computer Vision, Prague, Czech Republic,2004,291-303.
[58]Ronfard R, Schmid C, Triggs B, Learning to parse pictures of people, in: European Proc Of Int Conf on Computer Vision, Copenhagen, Denmark,2002, 27-31.
[59]Mikolajczyk K, Schmid D, A Zisserman, Human detection based on a probabilistic assembly of robust part detectors, in:European Proc Of Int Conf on Computer Vision, Prague, Czech Republic,2004,69-82.
[60]Ren X, Berg A.C, Malik J, Recovering human body configurations using pairwise constraints between parts, In:Proc Of Int Conf on Computer Vision, Beijing, China,2005,824-831.
[61]Ramanan D, Sminchisescu C, Tranining deformable models for localization, in: Computer Vision and Pattern Recognition, New York City, New York, USA, 2006,17-22.
[62]Hua G, Yang M H, Wu Y, Learning to estimate human pose with data driven belief propagation, in:Computer Vision and Pattern Recognition, San Diego, California, USA,2005,747-754.
[63]Felzenszwalb P, Huttenlocher D. Pictorial structures for object recognition. International Journal of Computer Vision,2005,61(1):55-79.
[64]Lan X, Huttenlocher D. Beyond trees:Common factor models for 2D human pose recovery. In:Proc Of Int Conf on Computer Vision.Beijing, China,2005: 470-477.
[65]Brand M, Shadow puppetry, in:Proc Of Int Conf on Computer Vision, Corfu, Greece,1999,1237-1244.
[66]Rosales R, Siddiqui M, Alon J,et al. Estimating 3D Body pose using uncalibrated cameras, in:Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii,2001,821-827.
[67]Howe N R, Silhouette lookup for automatic pose tracking, in:Workshop on Articulated and Non-Rigid Motion, Washington DC, USA,2004,15-22.
[68]Elgammal A, Lee C S, Inferring 3D body pose from silhouettes using activity manifold learning, in:Computer Vision and Pattern Recognition, Washington DC, USA,2004,681-688.
[69]Remondino F, Roditakis A.3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences. Pattern Recognition Lecture Notes in Computer Science 2003,2781:100-107.
[70]Zhang Z, Seah H S, Quah C K, et all. A Multiple Camera System with Real-Time Volume Reconstruction for Articulated Skeleton Pose Tracking. In: Proc of Int conference on Advances in multimedia modeling,2011, Taipei, Taiwan,182-192.
[71]Dong Y Q, Guilherme N. DeSouza. A new hierarchical particle filtering for markerless human motion capture. In:Proc of Int Computational Intelligence for Visual Intelligence, Nashville, TN,2009,68,343-356.
[72]Wan C K, Yuan B Z, Miao Z J, Model-based markerless human body motion capture using multiple cameras.In:Proc of Int Conference on Multimedia and Expo,2007, Beijing, China,1099-1102.
[73]Zhao X, Liu Y C. Generative tracking of 3D human motion by hierarchical annealed genetic algorithm.Pattern Recognition,2008,41(8):2470-2483.
[74]Starck J, Hilton A. Model-based human shape reconstruction from multiple views. Computer Vision and Image Understanding.2008,111(2):179-194.
[75]Aguiar DE, Stoll E, Theobalt C, et al. Performance capture from sparse multi-view video. ACM Transactions on Graphics.2008,27(3):1-10.
[76]Carranza J, Theobalt C, Magnor M, et al. Free-viewpoint video of human actors, in:ACM Special Interest Group for Computer GRAPHICS, San Diego,USA, 2003,565-577.
[77]Plankers R, Fua P, Articulated soft objects for multiview shape and motion capture. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003, 25 (9),1182-1187.
[78]Plankers R, Fua P, Tracking and modeling people in video sequences. Computer Vision and Image,2001,81(3):285-302.
[79]Deutscher J, Blake A, Reid I, Articulated body motion capture by annealed particle filtering, in:Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina,2000,126-133.
[80]Mitchelson J R, Hilton A, Hierarchical tracking of multiple people, in:Proc Of Int Conf British Machine Vision Conference, Norwich, UK, Sep,2003, 429-438.
[81]Wachter S, Nagel H H, Tracking persons in monocular image sequences, Computer Vision and Image Understanding,1999,74(3):174-192.
[82]Cristian C F, Josep R C, Montse P. Human motion capture using scalable body models. Computer Vision and Image Understanding.2011,11(5):1363-1374.
[83]Kaliamoorthi P, Kakarala R. Parametric annealing:a stochastic search method for human pose tracking. Pattern Recognition,2012,47(l):33-48.
[84]Mikic I, Trivedi M, Hunter E, et al. Human body model acquisition and tracking using voxel data. International Journal of Computer Vision,2003,53(3): 199-223.
[85]Shen J F, Yang W M, Liao Q M. Multiview human pose estimation with unconstrained motions. Pattern Recognition Letters,2011,32 (15):2025-2035
[86]Bandouch J, Jenkins O C, Beetz M. A self-training approach for visual tracking and recognition of complex human activity patterns. International Journal of Computer Vision,2012,99(2):166-189.
[87]Stauffer C, Grimson W E L, Adaptive background mixture models for real-time tracking, in:Computer Vision and Pattern Recognition, Santa Barbara, CA, 1998,246-252.
[88]Kristensen F, Nilsson P, Wall V O, Background segmentation beyond RGB, in: Asian Proc Of Int Conf on Computer Vision, Hyderabad, India,2006,13-16.
[89]Wren C R, Azarbayejani A, Darrell T, et al. Pfinder:real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19 (7):780-785.
[90]Cucchiara R, Grana C, Piccardi M, et al. Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25 (10):1337-1342.
[91]Zhao T, Nevatia R, Tracking multiple humans in complex situations, IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26 (9): 1208-1221.
[92]Takahashi K, Nagasawa Y, Hashimoto M. Remarks on 3D human posture estimation system using simple multi-camera system. In:Proc of Int Conf on Systems, Man and Cybernetics,2006:1962-1967.
[93]Prati A, Mikic I, Trivedi M M,et al. Detecting moving shadows:algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25 (7):918-923.
[94]Kim K, Chalidabhongse T H, Harwood D, et al. Real-time foreground-background segmentation using codebook model, Real-Time Imaging 11 (3) (2005) 172-185.
[95]Fihl P, Corlin R, Park S, et al, Tracking of individuals in very long video sequences, in:International Symposium on Visual Computing (ISVC), Stateline, Lake Tahoe, Nevada, USA,2006,6-11.
[96]Elgammal A, Harwood D, Davis L, Non-parametric model for background subtraction, in:European Proc Of Int Conf on Computer Vision, Dublin, Ireland, June 2000,751-767
[97]Haritaoglu I, Harwood D, Davis L S. Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(8):809-830.
[98]Heikkila M, Pietikainen M, 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.
[99]Oliver N, Rosario B, Pentland A, A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22 (8):831-843.
[100]Sheikh Y, Shah M, Bayesian modelling of dynamic scenes for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005, 27(11):1778-1792.
[101]Schindler K, Wang H, Smooth foreground-background segmentation for video processing, in:Asian Proc Of Int Conf on Computer Vision, LNCS 3852, Hyderabad, India,2006,581-590.
[102]Kalal Z, Matas J, and Mikolajczyk K. P-N learning:Bootstrapping binary classifiers by structural constraints. In:Computer Vision and Pattern Recognition,San Francisco, CA,2010:49-56
[103]McKenna S J, Jabri S, Duric Z, et al. Tracking interacting people, in:Proc Of Int Conf on Automatic Face and Gesture Recognition, Grenoble, France, 2000,348-353.
[104]Eng H L, Toh K A, Kam A H, J Wang, WY Yau, An automatic drowning detection surveillance system for challenging outdoor pool environments, in: Proc Of Int Conf on Computer Vision, Nice, France,2003,532-539.
[105]Chen M, Ma G, Kee S, Pixels classification for moving object extraction, in: Workshop on Motion and Video Computing (MOTION'05), Breckenridge, Colorado,2005,44-49.
[106]T Yang, S Z Li, Q Pan, J Li, Real-time multiple objects tracking with occlusion handling in dynamic scenes, in:Computer Vision and Pattern Recognition,San Diego, CA,2005,970-975.
[107]Figueroa P, Leite N, RML Barros, Background recovering in outdoor image sequences:an example of soccer players segmentation, Image and Vision Computing.2006,24 (4):363-374.
[108]Heikkila M, Pietikainen M, Heikkila J, A texture-based method for detecting moving objects, in:British Proc Of Int Conf Machine Vision Conference, London, UK, Sep 7-9,2004.
[109]齐玉娟,王延江,李永平.基于记忆的混合高斯背景建模.自动化学报,2010,36(11)：1520-1526.
[110]Stein A, Hoiem D, Hebert M. Learning to find object boundaries using motion cues. ICCV Hebert.2007:1-8.
[111]Malik J, Belongie S, Tleung, et al. Contour and texture analysis for image segmentation. International Journal of Computer Vision,2001,43(1):7-27.
[112]Comaniciu D, Meer P, Mean Shift:A robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence.2002,24(5):603-619.
[113]Martin D R, Fowlkes C C, Tal D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In:Proc Of Int Conf on Computer Vision, Vancouver, British Columbia,2001,416-423.
[114]Gutchess D, Trajkovic M, Solal E C, et al. A Jain, A background model initialization algorithm for video surveillance.International Conference on Computer Vision. Vancouver, BC,2001,1:733-740.
[115]Viola P, Jones M J, Snow D, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision,2005,63(2): 153-161.
[116]Lucas B D, Kanade T. An Interative Image Regitration Technique with an Application to Stereo Vision, Proc, DARPA Image Processing,1981:121-130
[117]Nagel H H, Displacement Vectors Derived From Second-Order Intensity Variation in Image Sequences, Computer Graphics Image,1983:85-117.
[118]Sidenbladh H. Detecting human motion with support vector machines. in:Proc Of Int Conf on Pattern Recognition, Cambridge, UK, Aug 2004,188-191.
[119]Gonzalez J J, Lim I S, Fua P, et al. Robust tracking and segmentation of human motion in an image sequence, in:Proc Of Int Conf on Acoustics, Speech, and Signal Processing, Hong Kong,2003,29-32.
[120]Sangi P, Heikkila J, O Silven, Extracting motion components from image sequences using particle filters, in:Scandinavian Proc Of Int Conf on Image Analysis, Bergen, Norway,2001,34-42.
[121]Bradski G R, Davis J W. Motion Segmentation and Pose Recognition with Motion History Gradients. Machine Vision and Applications 2002,13(3): 174-184.
[122]Kalal Z, Matas J, Mikolajczyk K. Online learning of robust object detectors during unstable tracking. In:Proc Of Int Conf on Computer Vision Workshops,Kyoto,2009:1417-1424.
[123]Kalal Z, Mikolajczyk K, Matas J. Tracking-Learning-Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7): 1409-1422.
[124]Comaniciu D, Ramesh V, Meer P, Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5): 564-575.
[125]Kang J, Cohen I, Medioni G, Persistent objects tracking across multiple non-overlapping cameras, in:Workshop on Motion and Video Computing (MOTION'05), Breckenridge, Colorado,2005,112-119.
[126]Yang C, Duraiswami R, Davis L, Fast multiple object tracking via a hierarchical particle filter, in:Proc Of Int Conf on Computer Vision, Beijing, China,2005,212-219.
[127]Capellades M B, Doermann D, DeMenthon D, et al. An appearance based approach for human and object tracking, in:Proc Of Int Conf on Image Processing, Barcelona, Spain,2003,85-93.
[128]Mittal A, Davis LS, M2Tracker:A multi-view approach to segmenting and tracking people in a cluttered scene. International Journal of Computer Vision. 2003,51(3):189-203.
[129]Park S, Aggarwal J K, Simultaneous tracking of multiple body parts of interacting persons, Computer Vision and Image Understanding,2006,102 (1): 1-21.
[130]Kolmogorov V, Criminisi A, Blake A, Cross G, Rother C. Bi-layer segmentation of binocular stereo video. In:Proc Of Int Conf on Computer Vision and Pattern Recognition. San Diego, CA USA,2005:407-414.
[131]钟凡,秦学英,陈佳舟,莫铭臻,彭群生.在线视频分割实时后处理.计算机学报.2009,32(2)：261-267.
[132]Haritaoglu I, Flickner M, Beymer D, Ghost3D:detecting body posture and parts using stereo, In:Proc of Int Workshop on Motion and Video Computing, 2002, Orlando, Florida,175-180.
[133]Ivanov Y A, Bobick A F, Liu J, Fast lighting independent background subtraction, International Journal of Computer Vision.2000.37 (2),199-207.
[134]Kinematic self retargeting:Aframework for human pose estimation Youding Zhu,Behzad Dariush,Kikuo Fujimura. Computer Vision and Image Understanding.2010,114(12):1362-1375.
[135]Iwase S, Saito H, Parallel tracking of all soccer players by integrating detected positions in multiple view images, in:Proc Of Int Conf on Pattern Recognition, Cambridge, UK,2004.
[136]Yang D B, Banos H H G, Guibas L J, Counting people in crowds with a real-time network of simple image sensors, in:Proc Of Int Conf on Computer Vision, Nice, France,2003,122-129.
[137]Diraco G, Leone A, Siciliano P. Human posture recognition with a time-of-flight 3d sensor for in-home applications. Expert Systems with Applications,2013,40(2):744-751.
[138]Schwarz L A, Mkhitaryan A, Mateus D, Navab N. Human skeleton tracking from depth data using geodesic distances and optical flow. Image and Vision Computing,2012,30(3):217-226.
[139]马颂德.计算机视觉：计算理论与算法基础.科学出版社,2003：72-75
[140]Zhang Z Y. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, 22(11):1330-1334.
[141]Stephen J S, Sriram J, Objection evaluation of approaches of skin detection using roc analysis, Computer Vision and Image Understanding,2007, 108(1-2):41-51.
[142]Bergh M V D, Koller M E, Haarlet-based hand gesture recognition for 3D interaction, Workshop on Applications of Computer Vision (WACV),2009, 66-72.
[143]Chai D, Ngan K N. Locating facial region of a head-and-shoulders color image. In:Proc Of Int Conf on Automatic Face and Gesture Recognition. Nara, Japan, 1998:124-129.
[144]Viola P,Jones M, Rapid object detection using a boosted cascade of simple features. In:Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii,2001,1-9.
[145]YoudingZhu, Behzad Dariush, Kikuo Fujimura. Kinematic self retargeting:A framework for human pose estimation. Computer Vision and Image Understanding.2010,114(12):1362-1375.
[146]Mikic I, Trivedi M, Hunter E, et al. Articulated body posture estimation from multi-camera voxel data, In:Computer Vision and Pattern Recognition, Kauai, Hawaii, USA,2001,455-460.
[147]Chu C W, Jenkins O C, Matari M J, Markerless kinematic model and motion capture from volume sequences, Proceedings of Computer Vision and Pattern Recognition.2,2003:475-482.
[148]Sun X P, Pan J B, Wei X P.3D mesh skeleton extraction using prominent segmentation. Computer Science and Information Systems.2010,7(1):63-74.
[149]Michoud B, Guillou E, Briceno H, et al. Real-Time Marker-free Motion Capture from multiple cameras. In Proc Of Int Conf on Computer Vision,2007, 1-7.
[150]Gavrila D M, SDavis L.3-D model-based tracking of humans in action:A multi-view approach. In:Comp Sociy conf on computer vision and pattern recognition,1996:73-80.
[151]Delamarre Q, Faugeras O,3D articulated models and multi-view tracking with physical forces, Computer Vision and Image Understanding,2001,81(3): 328-357.
[152]张磊.正交矩阵的反问题及其最佳逼近(The Inverse Problem of the Orthogonal Matrices and its Optimal Approximation)湖南数学年刊(Hunamn Annals of Mathematics).1990.10(1-2):122-126.
[153]孟纯军,胡锡炎.对称正交矩阵反问题及其最佳逼近.计算数学.2006, 28(3)：269-280.
[154]袁亚湘,孙文瑜.最优化理论与方法.科学出版社,第一版1997：108-121.
[155]吴敏.逆向工程中的多视定位算法研究.数据采集与处理,2003,29(3)：346-350.
[156]Zhang Z Y, Camera Calibration with One-Dimensional Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003, 26(7):892-899.
[157]Li M, Yang T, Xi R P, et al. Silhouette-based 2D human pose estimation. In:Proc Of Int Conf on Image and Graphics,2009, Xi'an, Shanxi, China, 143-148.
[2]Moeslund T B, Hilton A, Kruger V, A survey of advances in vision-based human motion capture and analysis [J]. Computer Vision and Image Understanding, 2006,104 (2),90-126.
[3]Poppe R, Vision-base human motion analysis:An overview [J]. Computer Vision and Image Understanding,2007,108 (1),4-18.
[4]Poppe R, A survey on vision-based human action recognition. Image and Vision Computing.2010,28:976-990.
[5]Weinland D, Ronfard R, Boyer E. A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding.2011,115:224-241.
[6]Loren A S D, Nassir N. Recognizing multiple human activities and tracking full-body pose in unconstrained environments. Pattern Recognition,2012, 45(1):11-23.
[7]Chen C, Yang Y, Nie F P.3D human pose recovery from image by efficient visual feature selection, Computer Vision and Image Understanding.2011, 115(3):290-299.
[8]Maggioni C, Kammerer B, Gesture Computer:history, design and applications, Computer Vision for Human-Machine Interaction. Cambridge University, Press, 1998.
[9]Freeman W, Weissman C, Television control by hand gestures. In:Proc Of Int Conf on Automatic Face and Gesture Recognition, Zurich, Switzerland,1995, 179-183.
[10]Marr D, Vision [M]. W. H. Freeman and Company,1982.
[11]吴朝福.计算机视觉中的数学方法.科学出版社.第一版,2008：94-95.
[12]Wang J, Cohen M F. Image and video matting:a survey. Computer Graphics and Vision.2007,3(2):97-175
[13]Rother C, Kolmogorov V, Blake A. GrabCut-Interactive foreground extraction using iterated graph cuts. In Proc. Siggraph, Los Angeles,2004,309-314.
[14]D.A. Forsythe, M.M. Fleck, Body plans, in:Computer Vision and Pattern Recognition, Puerto Rico,1997,678-683.
[15]Chen C,Zhuang Y T, Xiao J.Silhouette representation and matching for 3D pose discrimination-A comparative study. Image and Vision Computing.2010, 28(4):654-667.
[16]Caillette F. Real-time markerless 3-D human body tracking [dissertation] University of Manchester.2006:54-55.
[17]Dia-Mas L, Munoz-Salinas R, Madrid-Cuevas F J et al. Shape from silhouetteusing Dempster-Shafer theory. Pattern Recognition,2010,43(6): 2119-2131.
[18]Cheung K M, Baker S, Kanade T. Visual hull alignment and refinement across time:A 3D reconstruction Algorithm combining Shape-From-Silhouette with stereo, in:Computer Vision and Pattern Recognition, Madison, Wisconsin, USA,2003,375-382.
[19]Varol A, Shaji A, Salzmann M and P. Fua, Monocular 3D Reconstruction of Locally Textured Surfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(6):1118-1130.
[20]Berclaz J, Fleuret F, Turetken E and Fua P, Multiple Object Tracking using K-Shortest Paths Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33 (9):1806-1819.
[21]Fossati A, Dimitrijevic M, Lepetit V and Fua P, From Canonical Poses to 3-D Motion Capture using a Single Camera, IEEE Transactions on Pattern Analysis and Machine Intelligence,2010.32(7):1165-1181.
[22]Sigal L, Isard M, Haussecker H, et.al. Loose-limbed people:Estimating 3d human pose and motion using non-parametric belief propagation. International Journal of Computer Vision.2012,98:15-48.
[23]Vondrak M, Sigal L, Hodgins J K, et al, Video-based 3D Motion Capture through Biped Control. ACM Transactions on Graphics,2012.31(4):68-79.
[24]Daubney B, Gibson D, Campbell N. Estimating pose of articulated objects using low-level motion. Computer Vision and Image Understanding,2012,116: 330-346.
[25]Iosifidis A, Tefas A, Nikolaidis N, Pitas I. Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis. Computer Vision and Image Understanding,2012,116 (3):347-360.
[26]Yao B P, Khosla A, Li F F. Classifying action and measuring action similarity by modeling the mutual context of objects and human poses. In:Proc Of Int Conf on Machine Learning. Bellevue, WA, USA.2011,232-242.
[27]Zhao X, Fu Y, Ning H Z, Liu Y C, and et al. Human Pose Regression through Multiview Visual Fusion. IEEE Transactions on Circuits and Systems for Video Technology,2010,20(7):957-966.
[28]Yan J C, Song J, Liu Y C:Simultaneous 3D Human Motion Tracking and Voxel Reconstruction. Journal of Optical Engineering,2010,49(9):36-49.
[29]刘国军.基于可移动拍摄大场景下的人体运动跟踪关键技术的研究与应用[dissertation].哈尔滨工业大学,2009：2-21.
[30]Li K, Dai Q H, Xu W L. Markerless Shape and Motion Capture from Multiview Video Sequences. IEEE Transactions on Circuits and Systems for Video Technology,2011,21(3):320-334.
[31]Shen C F, Lin X Y, Shi Y C. Human pose estimation from corrupted silhouettes using a sub-manifold voting strategy in latent variable space. Pattern Recognition Letters,2009,30(4):421-431.
[32]Agarwa A, Triggs B, Recovering 3D human pose from monocular images, IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1):44-58.
[33]Navaratnam R, Fitzgibbon A, Cipolla R. Semi-supervised joint manifold learning for multi-valued regression. In:Proc Of Int Conf on computer vision, Rio de Janerio, Brasil.2007,1-8.
[34]Marcus A B, David J F, Aaron H. Physics-based person tracking using the anthropomorphic walker, International Journal of Computer Vision.2010, 87(1/2):140-155.
[35]Shakhnarovich G, Viola P, Darrell T, Fast pose estimation with parameter-sensitive hashing, in:Proc Of Int Conf on Computer Vision, Nice, France,2003,750-757.
[36]Sminchisescu C, Kanaujia A, Li Z. Discriminative density propagation for 3D human motion estimation. In:computer vision and pattern recognition,San Diego, CA, USA,2005,390-397.
[37]Urtasun R, Darrel T. Sparse probabilistic regression for activity-independent human pose inference. In:computer vision and pattern recognition, Anchorage, Alaska, USA,2008,1-8.
[38]Grauman K, Shakhnarovich G, T. Darrell, Inferring 3D structure with a statistical image-based shape model, in:Proc Of Int Conf on Computer Vision, Nice, France,2003,641-648.
[39]Rosales R, Sclaroff S. Learning body pose via specialized maps. Advances in neural information processing systems.2002,15,1263-1270.
[40]Sigal L, Black M J.Predicting 3D people from 2D pictures. In LNCS:IV Proc of Int Conf on articulated motion and deformable objects, Mallorca, Spain,2006, 185-195.
[41]Ju S, Black M J, & Yacoob, Y. Cardboard people:A parameterized model of articulated motion. In:Automatic Face and Gesture Recognition, Killington, VT, 1996,38-44.
[42]Wang P, Rehg J M. A modular approach to the analysis and evaluation of particle filters for figure tracking. In:computer vision and pattern recognition, New York, NY, USA,2006,790-797.
[43]Deutscher J, Reid I D. Articulated body motion capture by stochastic search. International Journal of Computer Vision, San Diego, CA, USA,2005, 185-205.
[44]Gall J, Rosenhahn B, Seidel H P. Clustered stochastic optimization for object recognition and pose estimation. Pattern Recognition,2007,4713:32-41.
[45]Gall J, Rosenhahn B, Brox T, Seidel H P. Optimization and filtering for human motion capture—A multi-layer framework. International Journal of Computer Vision,2010,87(1):75-92.
[46]Horaud R, Niskanen M, Dewaele G, et al. Human motion tracking by registering an articulated surface to 3-D points and normals. IEEE IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(1):158-163.
[47]John V, Ivekovic S, Trucco E. Articulated human motion tracking with HPSO. In:Proc Of Int Conf on computer vision theory and applications,2009,531-538.
[48]Kehl R, Gool L V. Markerless tracking of complex human motions from multiple views. Computer Vision and Image Understanding.2006, 104(2/3),190-209.
[49]Knossow D, Ronfard R, & Horaud R. Human motion tracking with a kinematic parameterization of extremal contours. International Journal of Computer Vision.2008,79(2):247-269.
[50]Sidenbladh H, Black M J, & Fleet, D.Stochastic tracking of 3D human figures using 2D image motion. In:European Proc Of Int Conf on Computer Vision, Dublin, Ireland,2000,702-718.
[51]MacCormick J, Isard M, Partitioned sampling, articulated objects, and interface-quality hand tracking, in:European Proc Of Int Conf on Computer Vision, Dublin, Ireland,2000,3-19.
[52]Ioffe S, Forsyth D, Human tracking with mixtures of trees, in:Proc Of Int Conf on Computer Vision, Vancouver, Canada,2001,690-695.
[53]Lee C S, Elgammal A. Coupled visual and kinematic manifold models for tracking, International Journal of Computer Vision.2010,87(1-2):118-139.
[54]Zou B J, Chen S, Cao S. Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking. Pattern Recognition,2009,42(7):1559-1571.
[55]Micilotta A, Ong E, Bowden R, Detection and tracking of humans by probabilistic body part assembly, in:Proc Of Int Conf British Machine Vision Conference, Oxford, UK,2005,429-438.
[56]Ramanan D, Forsyth D A, Zisserman A, Strike a pose:tracking people by finding stylized poses, in:Computer Vision and Pattern Recognition, San Diego, California, USA,2005,271-278.
[57]Roberts T J, McKenna S J, Ricketts I W, Human pose estimation using learnt probabilistic region similarities and partial configurations, in:European Proc Of Int Conf on Computer Vision, Prague, Czech Republic,2004,291-303.
[58]Ronfard R, Schmid C, Triggs B, Learning to parse pictures of people, in: European Proc Of Int Conf on Computer Vision, Copenhagen, Denmark,2002, 27-31.
[59]Mikolajczyk K, Schmid D, A Zisserman, Human detection based on a probabilistic assembly of robust part detectors, in:European Proc Of Int Conf on Computer Vision, Prague, Czech Republic,2004,69-82.
[60]Ren X, Berg A.C, Malik J, Recovering human body configurations using pairwise constraints between parts, In:Proc Of Int Conf on Computer Vision, Beijing, China,2005,824-831.
[61]Ramanan D, Sminchisescu C, Tranining deformable models for localization, in: Computer Vision and Pattern Recognition, New York City, New York, USA, 2006,17-22.
[62]Hua G, Yang M H, Wu Y, Learning to estimate human pose with data driven belief propagation, in:Computer Vision and Pattern Recognition, San Diego, California, USA,2005,747-754.
[63]Felzenszwalb P, Huttenlocher D. Pictorial structures for object recognition. International Journal of Computer Vision,2005,61(1):55-79.
[64]Lan X, Huttenlocher D. Beyond trees:Common factor models for 2D human pose recovery. In:Proc Of Int Conf on Computer Vision.Beijing, China,2005: 470-477.
[65]Brand M, Shadow puppetry, in:Proc Of Int Conf on Computer Vision, Corfu, Greece,1999,1237-1244.
[66]Rosales R, Siddiqui M, Alon J,et al. Estimating 3D Body pose using uncalibrated cameras, in:Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii,2001,821-827.
[67]Howe N R, Silhouette lookup for automatic pose tracking, in:Workshop on Articulated and Non-Rigid Motion, Washington DC, USA,2004,15-22.
[68]Elgammal A, Lee C S, Inferring 3D body pose from silhouettes using activity manifold learning, in:Computer Vision and Pattern Recognition, Washington DC, USA,2004,681-688.
[69]Remondino F, Roditakis A.3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences. Pattern Recognition Lecture Notes in Computer Science 2003,2781:100-107.
[70]Zhang Z, Seah H S, Quah C K, et all. A Multiple Camera System with Real-Time Volume Reconstruction for Articulated Skeleton Pose Tracking. In: Proc of Int conference on Advances in multimedia modeling,2011, Taipei, Taiwan,182-192.
[71]Dong Y Q, Guilherme N. DeSouza. A new hierarchical particle filtering for markerless human motion capture. In:Proc of Int Computational Intelligence for Visual Intelligence, Nashville, TN,2009,68,343-356.
[72]Wan C K, Yuan B Z, Miao Z J, Model-based markerless human body motion capture using multiple cameras.In:Proc of Int Conference on Multimedia and Expo,2007, Beijing, China,1099-1102.
[73]Zhao X, Liu Y C. Generative tracking of 3D human motion by hierarchical annealed genetic algorithm.Pattern Recognition,2008,41(8):2470-2483.
[74]Starck J, Hilton A. Model-based human shape reconstruction from multiple views. Computer Vision and Image Understanding.2008,111(2):179-194.
[75]Aguiar DE, Stoll E, Theobalt C, et al. Performance capture from sparse multi-view video. ACM Transactions on Graphics.2008,27(3):1-10.
[76]Carranza J, Theobalt C, Magnor M, et al. Free-viewpoint video of human actors, in:ACM Special Interest Group for Computer GRAPHICS, San Diego,USA, 2003,565-577.
[77]Plankers R, Fua P, Articulated soft objects for multiview shape and motion capture. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003, 25 (9),1182-1187.
[78]Plankers R, Fua P, Tracking and modeling people in video sequences. Computer Vision and Image,2001,81(3):285-302.
[79]Deutscher J, Blake A, Reid I, Articulated body motion capture by annealed particle filtering, in:Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina,2000,126-133.
[80]Mitchelson J R, Hilton A, Hierarchical tracking of multiple people, in:Proc Of Int Conf British Machine Vision Conference, Norwich, UK, Sep,2003, 429-438.
[81]Wachter S, Nagel H H, Tracking persons in monocular image sequences, Computer Vision and Image Understanding,1999,74(3):174-192.
[82]Cristian C F, Josep R C, Montse P. Human motion capture using scalable body models. Computer Vision and Image Understanding.2011,11(5):1363-1374.
[83]Kaliamoorthi P, Kakarala R. Parametric annealing:a stochastic search method for human pose tracking. Pattern Recognition,2012,47(l):33-48.
[84]Mikic I, Trivedi M, Hunter E, et al. Human body model acquisition and tracking using voxel data. International Journal of Computer Vision,2003,53(3): 199-223.
[85]Shen J F, Yang W M, Liao Q M. Multiview human pose estimation with unconstrained motions. Pattern Recognition Letters,2011,32 (15):2025-2035
[86]Bandouch J, Jenkins O C, Beetz M. A self-training approach for visual tracking and recognition of complex human activity patterns. International Journal of Computer Vision,2012,99(2):166-189.
[87]Stauffer C, Grimson W E L, Adaptive background mixture models for real-time tracking, in:Computer Vision and Pattern Recognition, Santa Barbara, CA, 1998,246-252.
[88]Kristensen F, Nilsson P, Wall V O, Background segmentation beyond RGB, in: Asian Proc Of Int Conf on Computer Vision, Hyderabad, India,2006,13-16.
[89]Wren C R, Azarbayejani A, Darrell T, et al. Pfinder:real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19 (7):780-785.
[90]Cucchiara R, Grana C, Piccardi M, et al. Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25 (10):1337-1342.
[91]Zhao T, Nevatia R, Tracking multiple humans in complex situations, IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26 (9): 1208-1221.
[92]Takahashi K, Nagasawa Y, Hashimoto M. Remarks on 3D human posture estimation system using simple multi-camera system. In:Proc of Int Conf on Systems, Man and Cybernetics,2006:1962-1967.
[93]Prati A, Mikic I, Trivedi M M,et al. Detecting moving shadows:algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25 (7):918-923.
[94]Kim K, Chalidabhongse T H, Harwood D, et al. Real-time foreground-background segmentation using codebook model, Real-Time Imaging 11 (3) (2005) 172-185.
[95]Fihl P, Corlin R, Park S, et al, Tracking of individuals in very long video sequences, in:International Symposium on Visual Computing (ISVC), Stateline, Lake Tahoe, Nevada, USA,2006,6-11.
[96]Elgammal A, Harwood D, Davis L, Non-parametric model for background subtraction, in:European Proc Of Int Conf on Computer Vision, Dublin, Ireland, June 2000,751-767
[97]Haritaoglu I, Harwood D, Davis L S. Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(8):809-830.
[98]Heikkila M, Pietikainen M, 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.
[99]Oliver N, Rosario B, Pentland A, A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22 (8):831-843.
[100]Sheikh Y, Shah M, Bayesian modelling of dynamic scenes for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005, 27(11):1778-1792.
[101]Schindler K, Wang H, Smooth foreground-background segmentation for video processing, in:Asian Proc Of Int Conf on Computer Vision, LNCS 3852, Hyderabad, India,2006,581-590.
[102]Kalal Z, Matas J, and Mikolajczyk K. P-N learning:Bootstrapping binary classifiers by structural constraints. In:Computer Vision and Pattern Recognition,San Francisco, CA,2010:49-56
[103]McKenna S J, Jabri S, Duric Z, et al. Tracking interacting people, in:Proc Of Int Conf on Automatic Face and Gesture Recognition, Grenoble, France, 2000,348-353.
[104]Eng H L, Toh K A, Kam A H, J Wang, WY Yau, An automatic drowning detection surveillance system for challenging outdoor pool environments, in: Proc Of Int Conf on Computer Vision, Nice, France,2003,532-539.
[105]Chen M, Ma G, Kee S, Pixels classification for moving object extraction, in: Workshop on Motion and Video Computing (MOTION'05), Breckenridge, Colorado,2005,44-49.
[106]T Yang, S Z Li, Q Pan, J Li, Real-time multiple objects tracking with occlusion handling in dynamic scenes, in:Computer Vision and Pattern Recognition,San Diego, CA,2005,970-975.
[107]Figueroa P, Leite N, RML Barros, Background recovering in outdoor image sequences:an example of soccer players segmentation, Image and Vision Computing.2006,24 (4):363-374.
[108]Heikkila M, Pietikainen M, Heikkila J, A texture-based method for detecting moving objects, in:British Proc Of Int Conf Machine Vision Conference, London, UK, Sep 7-9,2004.
[109]齐玉娟,王延江,李永平.基于记忆的混合高斯背景建模.自动化学报,2010,36(11)：1520-1526.
[110]Stein A, Hoiem D, Hebert M. Learning to find object boundaries using motion cues. ICCV Hebert.2007:1-8.
[111]Malik J, Belongie S, Tleung, et al. Contour and texture analysis for image segmentation. International Journal of Computer Vision,2001,43(1):7-27.
[112]Comaniciu D, Meer P, Mean Shift:A robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence.2002,24(5):603-619.
[113]Martin D R, Fowlkes C C, Tal D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In:Proc Of Int Conf on Computer Vision, Vancouver, British Columbia,2001,416-423.
[114]Gutchess D, Trajkovic M, Solal E C, et al. A Jain, A background model initialization algorithm for video surveillance.International Conference on Computer Vision. Vancouver, BC,2001,1:733-740.
[115]Viola P, Jones M J, Snow D, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision,2005,63(2): 153-161.
[116]Lucas B D, Kanade T. An Interative Image Regitration Technique with an Application to Stereo Vision, Proc, DARPA Image Processing,1981:121-130
[117]Nagel H H, Displacement Vectors Derived From Second-Order Intensity Variation in Image Sequences, Computer Graphics Image,1983:85-117.
[118]Sidenbladh H. Detecting human motion with support vector machines. in:Proc Of Int Conf on Pattern Recognition, Cambridge, UK, Aug 2004,188-191.
[119]Gonzalez J J, Lim I S, Fua P, et al. Robust tracking and segmentation of human motion in an image sequence, in:Proc Of Int Conf on Acoustics, Speech, and Signal Processing, Hong Kong,2003,29-32.
[120]Sangi P, Heikkila J, O Silven, Extracting motion components from image sequences using particle filters, in:Scandinavian Proc Of Int Conf on Image Analysis, Bergen, Norway,2001,34-42.
[121]Bradski G R, Davis J W. Motion Segmentation and Pose Recognition with Motion History Gradients. Machine Vision and Applications 2002,13(3): 174-184.
[122]Kalal Z, Matas J, Mikolajczyk K. Online learning of robust object detectors during unstable tracking. In:Proc Of Int Conf on Computer Vision Workshops,Kyoto,2009:1417-1424.
[123]Kalal Z, Mikolajczyk K, Matas J. Tracking-Learning-Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7): 1409-1422.
[124]Comaniciu D, Ramesh V, Meer P, Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5): 564-575.
[125]Kang J, Cohen I, Medioni G, Persistent objects tracking across multiple non-overlapping cameras, in:Workshop on Motion and Video Computing (MOTION'05), Breckenridge, Colorado,2005,112-119.
[126]Yang C, Duraiswami R, Davis L, Fast multiple object tracking via a hierarchical particle filter, in:Proc Of Int Conf on Computer Vision, Beijing, China,2005,212-219.
[127]Capellades M B, Doermann D, DeMenthon D, et al. An appearance based approach for human and object tracking, in:Proc Of Int Conf on Image Processing, Barcelona, Spain,2003,85-93.
[128]Mittal A, Davis LS, M2Tracker:A multi-view approach to segmenting and tracking people in a cluttered scene. International Journal of Computer Vision. 2003,51(3):189-203.
[129]Park S, Aggarwal J K, Simultaneous tracking of multiple body parts of interacting persons, Computer Vision and Image Understanding,2006,102 (1): 1-21.
[130]Kolmogorov V, Criminisi A, Blake A, Cross G, Rother C. Bi-layer segmentation of binocular stereo video. In:Proc Of Int Conf on Computer Vision and Pattern Recognition. San Diego, CA USA,2005:407-414.
[131]钟凡,秦学英,陈佳舟,莫铭臻,彭群生.在线视频分割实时后处理.计算机学报.2009,32(2)：261-267.
[132]Haritaoglu I, Flickner M, Beymer D, Ghost3D:detecting body posture and parts using stereo, In:Proc of Int Workshop on Motion and Video Computing, 2002, Orlando, Florida,175-180.
[133]Ivanov Y A, Bobick A F, Liu J, Fast lighting independent background subtraction, International Journal of Computer Vision.2000.37 (2),199-207.
[134]Kinematic self retargeting:Aframework for human pose estimation Youding Zhu,Behzad Dariush,Kikuo Fujimura. Computer Vision and Image Understanding.2010,114(12):1362-1375.
[135]Iwase S, Saito H, Parallel tracking of all soccer players by integrating detected positions in multiple view images, in:Proc Of Int Conf on Pattern Recognition, Cambridge, UK,2004.
[136]Yang D B, Banos H H G, Guibas L J, Counting people in crowds with a real-time network of simple image sensors, in:Proc Of Int Conf on Computer Vision, Nice, France,2003,122-129.
[137]Diraco G, Leone A, Siciliano P. Human posture recognition with a time-of-flight 3d sensor for in-home applications. Expert Systems with Applications,2013,40(2):744-751.
[138]Schwarz L A, Mkhitaryan A, Mateus D, Navab N. Human skeleton tracking from depth data using geodesic distances and optical flow. Image and Vision Computing,2012,30(3):217-226.
[139]马颂德.计算机视觉：计算理论与算法基础.科学出版社,2003：72-75
[140]Zhang Z Y. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, 22(11):1330-1334.
[141]Stephen J S, Sriram J, Objection evaluation of approaches of skin detection using roc analysis, Computer Vision and Image Understanding,2007, 108(1-2):41-51.
[142]Bergh M V D, Koller M E, Haarlet-based hand gesture recognition for 3D interaction, Workshop on Applications of Computer Vision (WACV),2009, 66-72.
[143]Chai D, Ngan K N. Locating facial region of a head-and-shoulders color image. In:Proc Of Int Conf on Automatic Face and Gesture Recognition. Nara, Japan, 1998:124-129.
[144]Viola P,Jones M, Rapid object detection using a boosted cascade of simple features. In:Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii,2001,1-9.
[145]YoudingZhu, Behzad Dariush, Kikuo Fujimura. Kinematic self retargeting:A framework for human pose estimation. Computer Vision and Image Understanding.2010,114(12):1362-1375.
[146]Mikic I, Trivedi M, Hunter E, et al. Articulated body posture estimation from multi-camera voxel data, In:Computer Vision and Pattern Recognition, Kauai, Hawaii, USA,2001,455-460.
[147]Chu C W, Jenkins O C, Matari M J, Markerless kinematic model and motion capture from volume sequences, Proceedings of Computer Vision and Pattern Recognition.2,2003:475-482.
[148]Sun X P, Pan J B, Wei X P.3D mesh skeleton extraction using prominent segmentation. Computer Science and Information Systems.2010,7(1):63-74.
[149]Michoud B, Guillou E, Briceno H, et al. Real-Time Marker-free Motion Capture from multiple cameras. In Proc Of Int Conf on Computer Vision,2007, 1-7.
[150]Gavrila D M, SDavis L.3-D model-based tracking of humans in action:A multi-view approach. In:Comp Sociy conf on computer vision and pattern recognition,1996:73-80.
[151]Delamarre Q, Faugeras O,3D articulated models and multi-view tracking with physical forces, Computer Vision and Image Understanding,2001,81(3): 328-357.
[152]张磊.正交矩阵的反问题及其最佳逼近(The Inverse Problem of the Orthogonal Matrices and its Optimal Approximation)湖南数学年刊(Hunamn Annals of Mathematics).1990.10(1-2):122-126.
[153]孟纯军,胡锡炎.对称正交矩阵反问题及其最佳逼近.计算数学.2006, 28(3)：269-280.
[154]袁亚湘,孙文瑜.最优化理论与方法.科学出版社,第一版1997：108-121.
[155]吴敏.逆向工程中的多视定位算法研究.数据采集与处理,2003,29(3)：346-350.
[156]Zhang Z Y, Camera Calibration with One-Dimensional Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003, 26(7):892-899.
[157]Li M, Yang T, Xi R P, et al. Silhouette-based 2D human pose estimation. In:Proc Of Int Conf on Image and Graphics,2009, Xi'an, Shanxi, China, 143-148.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |