基于步态的身份识别技术研究
|
摘要
步态(Gait)是指人行走时协调的、周期的姿态,是人体生物特征中的一种行为特征。近年来,基于步态的身份识别(Identification Recognition)方式,作为一种新兴的生物识别技术,以其独特性质如可远距离识别性、无接触性、容易采集、难于模仿等,引起了众多研究人员的重视。就现有的生物特征所处理的对象以及所面对的环境而言,任何一种生物特征的识别方法都不是完美无缺的,步态识别也无例外地遇到一些难题,如视点的变化、行人的衣着穿戴、行人是否拎包、较长时间上步态变化、有效的特征表征等,论文重点对步态运动目标提取,基于椭圆模型识别,基于步态轮廓和光流识别和基于局部线性嵌入流形降维步态识别方面进行了深入的研究。
在提取运动目标时,针对快速性计算,并较为准确的提取运动目标轮廓,提出了一种基于改进GVF和二阶差分阈值函数预测的模型,即根据二阶差分阈值函数预测初始化轮廓,改进的GVF模型使用线性映射规则进行二次初始化,迭代计算提取出运动目标,该计算模型同其他的GVF模型跟踪算法相比较,迭代次数少,速度更快；同时,为了提取出精确的运动目标轮廓,提出了基于模板匹配的运动目标提取模型,该方法可以更为精确的分割出人体目标,而且有利于消除伴随人体移动的阴影,为进一步更为有效地进行.步态识别提供了坚实的基础。
对于低分辨率运动目标轮廓的不连续性,提出了一种基于正交联系点改进的椭圆拟合方法,不需要提取目标的闭合轮廓。该方法使用关键帧而不是动态时间规划方式,降低了计算复杂度,拟合的效果良好,识别率比代数拟合方法更高。
为了更为有效地利用独特个性中的静态和动态的步态身份信息,提高正确识别率,提出了基于轮廓上八邻域像素光流进行步态识别算法。使用该模型研究发现：沿着目标的运动方向分量和综合指标分量正确识别率最高,几乎达到100%,而沿运动方向相反的分量部分识别率最低；基于轮廓上区域像素光流进行步态识别算法优于仅轮廓上像素进行识别的光流算法。
为了降低步态序列数据的复杂度,采用符合认知规律的流形算法,把高维数据本征结构映射到低维的空间进行识别,论文对基于局部线性嵌入(Locally Linear Embedding, LLE)非线性流形降维方法进行了详细研究,提出了一种基于加权距离进行识别的LLE模型。从正确识别率的稳定度上,从参数的选择容易度上,从正确识别率的高低上,该计算模型都要高于加权LLE和LLE算法,而且从计算时间考虑,提出的算法与LLE算法相当,但是低于加权LLE算法。
Gait is defined as the coordinated, cyclic combination of movements that result in human locomotion, and it is a behavior characteristic on human walking posture which belongs to one of human biological features. Recently, the technology for human identification recognition based on gait is attractive since it has the characteristics for example, no subject contact, easy collection at a distance and hard to disguise etc al. Existing biometric system are never perfect for all required performances and environments, therefore, gait faces some inevitable difficulties for data changes in different viewpoints, data changes for wearing clothing and package, data change for a long time, etc al. The paper aims to the human object abstraction, elliptical model recognition, optical flow effect on gait and dimensional reduction and the main contributions in detail as follows:
Aiming to rapidity for moving objection and accuracy contour, the algorithm of threshold function on second order difference and improved GVF is raised. The initial contour is established by threshold function, which is built by experience and displacement equation. Subsequently, the reinitial contour for improved GVF according to rectangle range is obtained by linear rules, and is used to extract moving object. The method compared to other GVF algorithm is not only simple, but also has little iterations and more rapidity. Aiming to the more accurate contour extraction in single view-point, a new method based on human template matching and improved GVF snake is proposed in the paper. It can segment object accuratly, eliminate the effect of shadow more effectively and make solid foundation for gait recognition.
Aiming to discontinuous moving object contour in low resolution images, a robust recognition algorithm based on improving ellipse model of orthogonal contacting points is raised, which need not closed object contour. A robust key fame, not dynatic time programming, makes detecting key fames easily and accurately. According to elliptic parameters in the key frames, correct classification rate(CCR) for gait recogntion is higher, and it reduces calculation complexity, and fitting result is better than algebraic one.
In order to benefit from static and dynamic information in walking posture and improve correct classification rate, an optical flow method based on pixels in neighbor area is proposed. The results display that the correct recognition rate(CCR) for compontes on syntheses and moving direction is higher to100percents almostly, and contrary moving direction along human walking has lowest CCR. On total, the raised optical flow algorithm in neighbor pixels is better than simple contour pixels.
Aiming to much information in gait sequence, manifold algorithm according with congnitive law is used to map the intrinsic dimension data in high dimension into lower dimension, in which is recognized. Therefore, locally linear embedding(LLE) manifild to reduce dimensionality is researched in detail, and an algorithm based on weighted distance testing and LLE train is raised. The expermental results show that the CCR of raised algorithm is higher than LLE and weighted LLE method, parameters for raised method are chosen easily, it runs rapidly than weighted LLE and is alike LLE in time.
在提取运动目标时,针对快速性计算,并较为准确的提取运动目标轮廓,提出了一种基于改进GVF和二阶差分阈值函数预测的模型,即根据二阶差分阈值函数预测初始化轮廓,改进的GVF模型使用线性映射规则进行二次初始化,迭代计算提取出运动目标,该计算模型同其他的GVF模型跟踪算法相比较,迭代次数少,速度更快；同时,为了提取出精确的运动目标轮廓,提出了基于模板匹配的运动目标提取模型,该方法可以更为精确的分割出人体目标,而且有利于消除伴随人体移动的阴影,为进一步更为有效地进行.步态识别提供了坚实的基础。
对于低分辨率运动目标轮廓的不连续性,提出了一种基于正交联系点改进的椭圆拟合方法,不需要提取目标的闭合轮廓。该方法使用关键帧而不是动态时间规划方式,降低了计算复杂度,拟合的效果良好,识别率比代数拟合方法更高。
为了更为有效地利用独特个性中的静态和动态的步态身份信息,提高正确识别率,提出了基于轮廓上八邻域像素光流进行步态识别算法。使用该模型研究发现：沿着目标的运动方向分量和综合指标分量正确识别率最高,几乎达到100%,而沿运动方向相反的分量部分识别率最低；基于轮廓上区域像素光流进行步态识别算法优于仅轮廓上像素进行识别的光流算法。
为了降低步态序列数据的复杂度,采用符合认知规律的流形算法,把高维数据本征结构映射到低维的空间进行识别,论文对基于局部线性嵌入(Locally Linear Embedding, LLE)非线性流形降维方法进行了详细研究,提出了一种基于加权距离进行识别的LLE模型。从正确识别率的稳定度上,从参数的选择容易度上,从正确识别率的高低上,该计算模型都要高于加权LLE和LLE算法,而且从计算时间考虑,提出的算法与LLE算法相当,但是低于加权LLE算法。
Gait is defined as the coordinated, cyclic combination of movements that result in human locomotion, and it is a behavior characteristic on human walking posture which belongs to one of human biological features. Recently, the technology for human identification recognition based on gait is attractive since it has the characteristics for example, no subject contact, easy collection at a distance and hard to disguise etc al. Existing biometric system are never perfect for all required performances and environments, therefore, gait faces some inevitable difficulties for data changes in different viewpoints, data changes for wearing clothing and package, data change for a long time, etc al. The paper aims to the human object abstraction, elliptical model recognition, optical flow effect on gait and dimensional reduction and the main contributions in detail as follows:
Aiming to rapidity for moving objection and accuracy contour, the algorithm of threshold function on second order difference and improved GVF is raised. The initial contour is established by threshold function, which is built by experience and displacement equation. Subsequently, the reinitial contour for improved GVF according to rectangle range is obtained by linear rules, and is used to extract moving object. The method compared to other GVF algorithm is not only simple, but also has little iterations and more rapidity. Aiming to the more accurate contour extraction in single view-point, a new method based on human template matching and improved GVF snake is proposed in the paper. It can segment object accuratly, eliminate the effect of shadow more effectively and make solid foundation for gait recognition.
Aiming to discontinuous moving object contour in low resolution images, a robust recognition algorithm based on improving ellipse model of orthogonal contacting points is raised, which need not closed object contour. A robust key fame, not dynatic time programming, makes detecting key fames easily and accurately. According to elliptic parameters in the key frames, correct classification rate(CCR) for gait recogntion is higher, and it reduces calculation complexity, and fitting result is better than algebraic one.
In order to benefit from static and dynamic information in walking posture and improve correct classification rate, an optical flow method based on pixels in neighbor area is proposed. The results display that the correct recognition rate(CCR) for compontes on syntheses and moving direction is higher to100percents almostly, and contrary moving direction along human walking has lowest CCR. On total, the raised optical flow algorithm in neighbor pixels is better than simple contour pixels.
Aiming to much information in gait sequence, manifold algorithm according with congnitive law is used to map the intrinsic dimension data in high dimension into lower dimension, in which is recognized. Therefore, locally linear embedding(LLE) manifild to reduce dimensionality is researched in detail, and an algorithm based on weighted distance testing and LLE train is raised. The expermental results show that the CCR of raised algorithm is higher than LLE and weighted LLE method, parameters for raised method are chosen easily, it runs rapidly than weighted LLE and is alike LLE in time.
引文
[1]Ross A, Jain A K. Human recognition using biometrics:an overview. Annals of Telecommunications.2007,62(1/2):11-35P
[2]朱珍,王景艳.生物识别技术与应用.佛山科学技术学院学报(自然科学版).2003,21(3)：66-69P
[3]Jain A K, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology.2004,14(1):4-20P
[4]Stroica P, Vladescu M. An overview on biometric identification methods and implementations. Proceding of SPIE.2010,7821:78211T-1-78211T-4P
[5]Boyd J E, Little J J. Biometric gait recognition. Advanced Studies in Biometrics. 2003.19-42P
[6]Johansson G. Visual perception of biological motion and a model for its analysis. Perception and Psychophysics 1973,14:201-211P
[7]Johansson G. Visual motion perception. Scientific American,1975:76-88P
[8]Cohen L, Shipley T F, Marshark E, Taht K, Aster D. Detecting animals in point-light displays. In:Twenty Second Annual Meeting of the Cognitive Science Society, Philadelphia, PA.2000:70P
[9]Cutting J E, Kozlowski L T. Recognizing friends by their walk:gait perception without familiarity cues. Bulletin of the Psychonomic Society.1977,9:353-356P
[10]Niyogi S A, Adelson E H. Analyzing and recognizing walking figures in XYT. IEEE Conference on Computer Vision and Pattern Recognition.1994:469-474P
[11]Nixon M S, Carter J N, Nash J M, Huang P S, Cunado D, Stevenage S V. Automatic gait recognition. Kluwer Academic Publishers.1999:231-250P
[12]Rohila N, Kumar B, Chauhan N. Abnormal gait recognition. International Journal on Computer Science and Engineering.2010,2(5):1544-1551P
[13]Larsen P K, Simonsen E B, Lynnerup N. Gait analysis in forensic medicine. Journal of Forensic Sciences.2008,53:1149-1153P
[14]Carlo F, Paolo C. Multichannel SEMG in clinical gait analysis:A review and state-of-the-art. Clinical Biomechanics.2009,24(3):236-245P
[15]Leu A, Ristic-Durrant D, Graser A. A robust markerless vision-based human gait analysis system.6th IEEE International Symposium on Applied Computational Intelligence and Informatics,2011, Timisoara, Romania:415-420P
[16]Nixon M S, Carter J N. Automatic recognition by gait. Proc. IEEE.2006, 94(11):2013-2024P
[17]Haritaoglu I, Cutler R, Harwood D, Davis L S. Backpack:detection of people carrying objects using silhouettes. Computer Vision and Image Understanding.2001,81(3): 385-397P
[18]Boulgouris N V, Hatzinakos D, Plataniotis K N. Gait recognition:a challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine, 2005,22(6):78-90P
[19]Li Y B, Jiang T X, Qiao Z H, Qian H J. General methods and development actuality of gait recognition. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China.2007:2-4P
[20]Gafurov D. A survey of biometric gait recognition:approaches, security and challenges. in Proceedings of Annual Norwegian Computer Science Conference,2007, http://www.iu.hio.no/nik07/bidrag/Gafurov.pdf.
[21]陈昌红,梁继民,赵恒,焦李成.步态表征和步态融合方法新进展.计算机科学.2010,37(8)：15-20页
[22]Wang J, She M, Nahavandi S. A review of vision-based gait recognition methods for human identification[C], IEEE Proceedings of the Digital Image Computing:Techniques and Application, Piscataway, N.J,2010, pp:320-327.
[23]Nixon M. S, Carter J N, Shutler J, Grant M. New advances in automatic gait recognition. Elsevier Information Security Technical Report,2002,7 (4):23-35P
[24]Zhang Z X, Hu M D, Wang Y H. A survey of advances in biometric gait recognition. Lecture Notes in Computer Science:Biometric Recognition.2011,7098:150-158P
[25]贲睍烨,徐森,王科俊.行人步态的特征表达及识别综述.模式识别与人工智能,2012,25(1)：71-81
[26]Haritaoglu I, Davis L S, Harwood D. W4 who? when? where? what? a real time system for detecting and tracking people. In:Proc. IEEE International Conference on Automatic Face and Gesture Recognition.1998:222-227P
[27]Long W, Yang Y. Stationary background generation:An alternative to the difference of two images. Pattern Recognition.1990,23(12):1351-1359P
[28]Kornprobst P, Deriche R. Aubert G. Image sequence analysis via partial difference equations. Journal of Mathematical Imaging and Vision.1999.11(1):5-26P
[29]Rtlssell D, Gong S. A highly efficient block-based dynamic background model. Proc of the IEEE Conference on advanced Video and Signal Based Surveillance. London. UK, 2005:417-422P
[30]杨广林,孔令富.基于图像分映的背景模型构建方法.机器人.2007,29(1)：29-34P
[31]Heikkila M, Pietikainen M. A texture based method for modeling the background and detecting moving objects. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2006,28(4):657-662P
[32]Halevy G, Weinshall D. Motion of disturbances:detection and tracking of multi-body non-rigid motion. Machine Vision and Applications.1999,11(3):122-137P
[33]Elgammal A, Duraiswami R, Davis L. Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence.2003,25(11):1499-1504P
[34]Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection. IEEE Transaction on Pattern Recognition and Machine Intelligence.2005,27(11):1778-1792P
[35]Power P W, Schoonees J A. Understanding background mixture models for foreground segmentation. In:Proc. Image and Vision Computing, Auckland, New Zealand, 2002:267-271 P
[36]Lee D S, Hull J, ErolB. A bayesian framework for gaussian mixture background modeling. In:Proc. IEEE International Conference on Image Processing,2003:973-976P
[37]Dar-shyang Lee. Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(5):827-832P
[38]KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. In:Proc. European Workshop Advanced Video Based Surveillance Systems.2001,1(3):1-5P
[39]Cheng J. Flexible background mixture models for foreground segmentation. Image and Vision Computing.2006,24:473-482P
[40]Rittseher J, Kato J, Joga S. A probabilistic background model for tracking. Proc of the 6th European Conference on Computer Vision. Hilton Head Island, USA,2000, Ⅱ: 255-261P
[41]Prati A, Mikic I, Trivedi M. Detecting moving shadows:formulation, algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(7): 918-924P
[42]Bai S C, Yun Q L. Indoor and outdoor people detection and shadow suppression by exploiting HSV color information. In:Proc. the 4th International Conference on Computer and Information Technology. Wuhan, China,2004:137-142P
[431 Leone A, Distante C, Buccolieri F. A texture-based approach for shadow detection. IEEE Conference on Advanced Video and Signal Based Surveillance.2005:371-376P
[44]Stauder J, Mech R, Ostermann J. Detection of moving cast shadows for object segmentation. IEEE Transactions on Multimedia.1999,1(1):65-76P
[45]Martel-Brisson N, Zaccarin A. Learning and removing cast shadows through a multidistribution approach. IEEE Transactions on Pattern Analysis and Machine Intelligence.2007,29(7):1133-1146P
[46]王智灵,周路平,陈宗海.针对不同信息特征的鲁棒背景建模技术分析.模式识别与人工智能.2009,22(2)：240-245P
[47]丁亮,张永平,张雪英.图像分割方法及性能评价综述.2010,31(12)：78-83P
[48]Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision. In:Proc. the 7th International Joint Conference on Artificial Intelligence, Vancouver,1981:674-679P
[49]Horn B K P, Schunck B G. Determining optical flow. Artificial Intelligence.1981, 17(1):185-204P
[50]Wren C. R, Azarbayejani A, Darrell T. Pfinder:real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997,19(7):780-785P
[51]Masoud O, Papanikolopoulos N P. A novel method for tracking and counting pedestrians in real time using a single camera. IEEE Transactions on Vehicular Technology.2001, 50(5):1267-1278P
[52]Dockstader S L, Tekalp A M. Multiple cameras tracking of interacting and occluded human motion. Proceedings of the IEEE.2001,89(10):1441-1455P
[53]McKenna S, Jabri S, Duric Z. Tracking groups of people. Computer Vision and Image Understanding.2000,80(1):42-56P
[54]Zhao T, Nevatia R. Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9),pp:1208-1221.
[55]Bar-Shalom Y, Fortmann T. Tracking and data association. Academic Press,1988.
[56]王勇.基于统计方法的运动目标检测与跟踪技术研究.华中科技大学博士学位.2009：11-18页
[57]Jang D S, Choi H I. Active models for tracking moving objects. Pattern Recognition. 2000,33(7):1135-1146P
[58]Har H I K, Schonfeld D. Fast object tracking using adaptive block matching. IEEE Transactions on Multimedia.2005,7 (5):853-859P
[59]Wren C R, Azarbayejan I A, Darrel L, Pentland A P. Pfinder:real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997.19 (7):780-785P
[60]Lu J, Hiroyasu I, KYoko H. A hybrid vision method for autonomous guided vehicle navigation. ACTA Automatic Sinica.2006,32(6):988-998P
[61]Birchfield S. Elliptical head tracking using intensity gradients and color histograms. Proc of IEEE Conference on Computer Vision and Pattern Recognition.1998:232-237P
[62]Dutta A, Kar A, Chatterji B N. Corner detection algorithms for digital images in last three decades. IETE Technical Review.2008,25(3):123-133P
[63]Chen Z, Birchfild S T. Person following with a mobile robot using binocular feature-based tracking. IEEE/RSJ International Conference on Intelligence Robots and Systems (IROS 2007). San Diego,2007:815-820P
[64]杨育彬,林珲.利用天文观测图像对空间碎片目标进行自动识别与追踪.武汉大学学报(信息科学版).2010,35(2)：209-214P
[65]Yokoyama M, Poggio T. A contour-based moving object detection and tracking.2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking,2005.
[66]Jonas D V, Daniel O, Wilfried P, Laury C, Dominique V D S. Tracking multiple objects using moving snakes.16th International Conference on Digital Signal Processing, Proceedings, IEEE Computer Society,445 Hoes Lane-P.O.Box 1331, Piscataway, USA, 2009
[67]杨涛,李静,潘泉,张艳宁.一种基于贪心搜索的实时多目标遮挡处理算法.自动化学报.2010,36(3)：375-384页
[68]Smith C E. Fast tracking of natural textures using fractal snakes.2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010, Istanbul, Turkey,2010:2175-2181P
[69]Wang C, Dai G. Moving targets detection and tracking based on bayesian foreground segmentation and GVF-snake.3rd International Workshop on Advanced Computational Intelligence, IWACI 2010. Suzhou, China,2010:565-569P
[70]Aksel A, Acton S. Target tracking using the snake particle filter. Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation. Institute of Electrical and Electronics Engineers Inc., Austin, USA,2010:33-36P
[71]董春利,董育宁,刘杰.基于粒子滤波和GVF Snake的目标跟踪算.仪器仪表学报.2009,30(4)：828-833页
[72]Chen Z, Lee H J. Knowledge-guided visual perception of 3D human gait from a single image sequence. IEEE Transactions on Systems, Man and Cybernetics. 1992,22(2):336-343P
[73]Ju S X, Black MJ, Yacoob Y. Cardboard people:a parameterized model of articulated image motion. Proc of the 2nd IEEE International Conference on Automatic Face and Gesture Recognition. Washington DC:IEEE Computer Society,1996:38-44页
[74]Wachter S, Nageltt H H. Tracking of persons in monocular image sequences. Computer Vision on Image Understanding.1999,74(3):174-192P
[75]Bergh M, Koller-meier E, Gool L. Real-time body pose recognition using 2D or 3D haarlets. International Journal of Computer Vision 2009,83(1):72-84P
[76]Zhang R, Vogler C, Metaxas D. Human gait recognition. IEEE Conference on Computer Vision and Pattern Recognition.2004:18-25P
[77]杨军,吴晓娟,彭彰,陈文刚.基于多区域分割的步态表示与识别算法研究.计算机学报.2006,29(10)：1876-1881页
[78]Lee L, Grimson W. Gait analysis for recognition and classification. International Conference on Automatic Face and Gesture Recognition.2002:155-162P
[79]Bhanu B, Han J. Individual recognition by kinematic-based gait analysis.16th Proceedings of International Conference on Pattern Recognition. Quebec, Canada,2002,3: 343-346P
[80]Kaziska, D, Srivastava A. Joint gait-cadence analysis for human identification using an elastic shape framework. Communications in Statistics-Theory and Methods.2010, 39(10):1817-1831P
[81]Zhao G, Liu G, Li H, Pietikainen M.3D gait recognition using multiple camera. International Conference on Automatic Face and Gesture Recognition.2006:529-534P
[82]Nash J M, Carter J N, Nixon M S. Dynamic feature extraction via the velocity hough transform. Pattern Recognition Letters.1997,18(10):1035-1047.
[83]Su S Z, Wang L, Li S Z. Interframe variation vector:a novel feature for gait recognition. Journal of Donghua University (English Edition).2010,27(2):233-236P
[84]Li Y, Yang Q. Gait recognition algorithm based on the tiptoe trajectory. Journal of Computational Information Systems.2010,6(13):4493-4502P
[85]Kim D, Paik J. Gait recognition using active shape model and motion prediction. IET Computer Vision.2010,4(1):25-36P
[86]贾楷熙,薛静.基于Zernike矩和BP网络的步态识别技术研究.西北工业大学学学报.2010,28(5)：669-673页
[87]Han S. Liao Z W, Chen G Y. A gait recognition method using L1-PCA and LDA. Proceedings of the Eighth International Conference on Machine Learning and Cybernetics. Baoding,2009:3198-3200P
[88]Han J, Bhanu B. Statistical feature fusion for gait-based human recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA,2004:842-847P
[89]Liu J, Zheng N. Gait history image:a novel temporal template for gait recognition. IEEE International Conference on Multimedia and Expo. Xi-an, China,2007:663-666P
[90]Lam T H W, Cheung K H, Liu J N K. Gait flow image:A silhouette-based gait representation for human identification. Pattern Recognition.2011,44(4):973-987P
[91]Xu S L, Zhang Q J. Gait recognition using fuzzy principal component analysis.2nd International Conference on e-Business and Information System Security, EBISS. Wuhan, China,2010:10-13P
[92]Lee S, Liu Y, Collins R. Shape variation-based frieze pattern for robust gait recognition. IEEE International Conference on Computer Vision and Pattern Recognition. Minneapolis, MN,2007:1-8P
[93]Chen C H, Liang J, Zhao H, Hu H, Tian J. Factorial HMM and parallel HMM for gait recognition. IEEE Transactions on Systems, Man and Cybernetics, Part C.2009,39(1): 114-123P
[94]Kusakunniran W, Wu Q. Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron. Pattern Recognition Letters.2011, In Press.
[95]Goffredo M, Bouchrika I, Carter J N, Nixon M S. Self-calibrating view-invariant gait biometrics. IEEE Transactions on Systems, Man and Cybernetics, Part B.2010, 40(4):997-1008P
[96]Goffredo M, Bouchrika I, Carter J, Nixon M. Performance analysis for automated gait extraction and recognition in multi-camera surveillance. Multimedia Tools and Applications.2010,50(1):75-94P
[97]Su H, Chen G Y. A new method of gait recognition independent of view angle. Proceedings of the Ninth International Conference on Machine Learning and Cybernetics. Qingdao,2010:3091-3093P
[98]Liu H, Cao Y, Wang Z. A novel algorithm of gait recognition. International Conference on Wireless Communication& Signal Processing,2009:1-5P
[99]Kusakunniran W, Wu Q, Zhang J, Li H. Support vector regression for multi-view gait recognition based on local motion feature selection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco,2010:974-981P
[100]Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y. Gait recognition using a view transformation model in the frequency domain. In Europeon Conference on Computer Vision.2006:151-163P
[101]Lu J, Tan Y P. Uncorrelated discriminant simplex analysis for view-invariant gait signal computing. Pattern Recognition Letters,2010,31(5):382-393P
[102]Gu J, Ding X, Wang S, Wu Y. Action and gait recognition from recovered 3-D human joints. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, 2010,40(4):1021-1033P
[103]Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N. View-independent human motion classification using image-based reconstruction. Image and Vision Computing.2009,27(8):1194-1206P
[104]Liu N, Lu J, Tan Y P. Joint subspace learning for view-Invariant gait recognition. IEEE Signal Processing Letters.2011,18(7):431-434P
[105]Wang L, Ning H, Tan T. Fusion of static and dynamic body biometrics for gait recognition. IEEE Transactions on oil Circuit and Systems for Video Technology.2004, 15(2):149-158P
[106]Chai Y, Wang Q, Jia J. A novel gait recognition method via fusing shape and kinematics features. International Conference on Multimedia and Expo. Springer Berlin,2006: 80-89P
[107]Jain A, Dass S, Nandakumar K. Can soft biometric traits assist user recognition? Proceedings of SPIE.2004,5404:561-572P
[108]Moustakas K, Tzovaras D. Gait recognition using geometric features and soft biometrics. IEEE Signal Processing Letters.2010,17(4):367-370P
[109]Yun J. User identification using gait patterns on UbiFloorⅡ. Sensors.2011,11(3): 2611-2639P
[110]徐苏.基于步态触觉特征的生物特征识别.中国科学技术大学博士论文集.2010.
[111]李轶,明东,王璐.綦宏志,万柏坤.融合步态和人脸特征的远距离身份识别研究.仪器仪表学报.2011,32(2)：264-270页
[112]Geng X, Smith-Miles K. Context-aware fusion:A case study on fusion of gait and face for human identification in video. Pattern Recognition.2010,43(10):3660-3673P
[113]Zhao X, Du Y. Research on gait-based human identification. ICCASM, Shanxi, Taiyuan.2010:12480-12482P
[114]Zhang J, Liu Z. Real-time gait classification based on fuzzy associative memory. International Journal of Modelling, Identification and Control.2010,10(3-4):263-271P
[115]Aravind S, RoyChowdhuiy A, Chellappa R. A hidden markov model based framework for recognition of humans from gait sequences. International Conference on Image Processing.2003,2:93-96P
[116]Nizami I. F, Hong S. Automatic gait recognition based on probabilistic approach. International Journal of Imaging Systems and Technology.2010,20(4):400-408P
[117]Lee C S, Elgammal A. Style adaptive contour tracking of human gait using explicit manifold models. Machine Vision and Applications.2010:1-18P
[118]李颖佩,卢结成,徐玉辉.基于Zernike矩快速算法的步态识别.计算机仿真.2010,27(11)：238-241页
[119]Moon H, Phillips P J. Computational and performance aspects of PCA-based face-recognition algorithms. Perception.2001,30:303-321P
[120]Phillips P J, Moon H, Rizvi S A, Rauss P J. The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern analysis and Machine Intelligence.2000,22(10):1090-1104P
[121]郑南宁.计算机视觉与模式识别,国防工业出版社.1998：192-198页
[122]邵文坤,黄爱民,韦庆.动态场景下的运动目标跟踪方法研究.计算机仿真.2006,23(5)：181-184页
[123]Gibson J J. The perception of the visual world. Houghton Mifflin Publishers, Boston. 1950:25-28P
[124]章毓晋编著.图像工程(上册).北京：清华大学出版社,1999：67-71页
[125]Beauchemin S S, Barron J L. The computation of optical flow. ACM Computing.1995, 27(3):433-466P
[126]Fazekas S, Chetverikov D. Dynamic texture recognition using optical flow features and temporal periodicity. Content-Based Multimedia Indexing,2007:25-32P
[127]Quan H, Wang C. Overview of dynamic texture recognition techqiques based on optical flow. International Conference on Computational Intelligence and Software Engineering. 2009:1-4P
[128]Anandan P. A computational framework and algorithm for the measurement of visual motion. International Journal of Computer Vision.1989,2(3):283-310P
[129]Fleet D J, Jepson A D, Jenkin M R M. Phase-based disparity measurement. CVGIP: Image Understanding.1991,53(2):198-210P
[130]Singh A. Optic flow computation:a unified perspective. Piscataway, NJ:IEEE Computer Society Press,1992.
[131]Weber J, Malik J. Robust computation of optical flow in a multi-scale differential framework. International Journal of Computer Vision.1995,14:67-81P
[132]Gastounioti A, Golemati S, Tsiaparas N N, Stoitsis J S, Nikita K S. Multiscale approach for weighted least-squares optical flow for estimating arterial wall displacements.2011 10th International Workshop on Biomedical Engineering, Kos Island, Greece.2011:1-4P
[133]Brox T, Malik J. Large displacement optical flow:descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence.2011, 33(3):500-513P
[134]Ince S, Konrad J. Occlusion-aware optical flow estimation. IEEE Transactions on Image Precessing.2008,17(8):1443-1451P
[135]Seegmiller N, Wettergreen D. Optical flow odometry with robustness to self-shadowing. International Conference on Intelligent Robots and Systems,2011, San Francisco, California:613-618P
[136]Itohara T, Otsuka T, Mizumoto T, Ogata T, Okuno H. G. Particle-filter based audio-visual beat-tracking for music robot ensemble with human guitaris. International Conference on Intelligent Robots and Systems,2011, San Francisco, California:118-124P
[137]Song X, Seneviratne L.D, Althoefer K. A kalman filter-integrated optical flow method for velocity sensing of mobile robots. IEEE/ASME Transactions on Mechatronics, 2011,16(3),pp:551-563.
[138]Schmudderich J, Willert V, Eggert J, Rebhan S, Goerick C, Sagerer G, Korner E. Estimating object proper motion using optical flow, kinematics, and depth information. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics.2008, 38(4):1139-1151P
[139]姜浩,郁道银,孙正,徐智,黄家祥.光流法在冠状动脉运动分析中的应用.光电子激光.2004,15(9)：1128-1131页
[140]Tanaka A, Yoshifumi S. Blood flow visualization of left atrial spontaneous echo contrast(SEC) using gradient based optical flow estimation. Proceedings of the 29th Annual International Conference of the IEEE EMBS cite International, Lyon, France. 2007:4500-4503P
[141]Burlina p, Hoffmann B, Abraham T. Computing lest ventricular hemodynamics from echographic optical flow of CEUS microspheres. Life Science Systems and Applications Workshop.2011:67-72P
[142]Danilouchkine M, Mastik F, vander S A. Improving IVUS palpography by incorporation of motion compensation based on block matching and optical flow. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.2008, 55(11):2392-2404P
[143]Dawood M, Buther F, Jiang X, Schafers K P. Respiratory motion correction in 3-D PET data with advanced optical flow algorithms. IEEE Transactions on Medical Imaging. 2008,27(8):1164-1175P
[144]Mitiche A, Sekkati. H. Optical flow 3D segmentation and interpretation:a variational method with active vurve evolution and level sets. IEEE Tansactions on Pattern Analysis and Machine Intelligence.2006,28:1818-1829P
[145]Schwarz L A, Mkhitaryan A, Mateus D, Navab N. Estimating human 3D pose from time-of-flight images based on geodesic distances and optical flow. IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, Santa Barbara, California on March 21-25,2011:700-706P
[146]Romero H, Salazar S, Lozano R. Real-time stabilization of an eight-rotor UAV using optical flow. IEEE Transactions on Robotics.2009,25(4):808-817P
[147]Michalke T P, Stein F, Franke U. Towards a closer fusion of active and passive safety: Optical flow-based detection of vehicle side collisions. Intelligent Vehicles Symposium (IV),2011 IEEE, Alcala de Henares, Spain.2011:181-188P
[148]Black M J, Anandan P. The robust estimation of multiple motions:parametic and piece wise-smooth flow fields. Compufer vision and image understanding.1996,63(1): 75-104P
[149]Liao S H, Liu B Y. An edge-based approach to improve optical flow algorithm[C],3rd International Conference on Advanced Computer Theory and Engineering,2010,6: 45-51P
[150]Manchester I R, Savkin A V, Faruqi F A. Method for optical-flow-based precision missile guidance. IEEE Transactions on Aerospace and Electronic Systems. 2008,4(3):835-851P
[151]Luo R C, Huang C Y, Lin P H. Alignment and tracking of facial features with component-based active appearance models and optical flow. Advanced Intelligent Mechatronics (AIM),2011 IEEE/ASME International Conference on, Budapest, Hungary, 2011:1058-1063P
[152]Hsieh C K, Chen Y C. An optical flow-based approach to robust face recognition under expression variations. IEEE Transactions on Image Processing.2009,19(1):233-240 P
[153]Wang J Y A, Adelson E H. Representing moving images with layers, IEEE Transactions on Image Process.1994,3(5):625-638P
[154]焦春林,高满屯,曹秋萍,陈淑梅.基于光流场的图像分割.西北工业大学学报. 2006,24(2)：265-269页
[155]Horn B K P. Robot Vision]. Cambridge, MA:MIT Press,1986:165-178P
[156]Jain A K. Fundamentals of digital image processing, Englewood Cliffs, NJ: Prentice-Hall,1989:311-315P
[157]Amini A A, Tenran S, Weymouth T E. Using dynamic programming for minimizing the energy of active contours in the presence of hard constrains. Proceeding of the 2nd International Conference of Computer Vision.1988:95-99P
[158]黄志彦,曹春红,徐剑.基于小波多尺度分析与GVF Snake的空中目标提取.光电与控制.2002,9(4)：33-36页
[159]王立功,于甬华,姜晓彤,罗立民.基于Snake模型的图像目标轮廓自动跟踪方法.东南大学学报(自然科学版).2003,33(2)：215-218页
[160]罗希平,田捷.一种改进的交互式医学图像序列分割方法.电子学报.2003,31(1)：29-32页
[161]郭礼华,李建华,杨树唐.基于运动补偿的Snake视频对象跟踪算法.红外与激光工程.2005,34(1)：93-97页
[162]邱书博,王化祥,梁志伟.一种新的B-Snake算法在目标轮廓跟踪中的应用.中国图象图形学报.2005,10(5)：585-589页
[163]Wren C R, Azarbayejani A, Darrell T, Pentland A P. Pfinder:real-time tracking of human body. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997, 19(7):780-785P
[164]Dong C L, Dong Y N. A novel algorithm for object tracking with particle filtering and GVF snake. Eighth International Conference on Intelligent Systems Design and Applications.2008:8-13P
[165]Rathi Y, Vaswani N, Tannenbaum A. Tracking deformable objects using particle filtering for geometric active contours. IEEE Trans. Pattern Analysis and Machine Intelligence,2007,29(8):1470-1475P
[166]Betser A, Vela P, Tannenbaum A. Automatic tracking of flying vehicles using geodesic snakes and Kalman filtering.43rd IEEE Conference on Decision and Control.2004,2: 1649-1654P
[167]Khansari M, Rabiee H R, Asadi M. A robust object shape prediction algorithm in the presence of White Gaussian Noise. Proceedings of 12th International Multi-Media Modelling Conference.2006,1:4-6P
[168]Ivana M, Slawomir K, James D T. Segmentation and tracking in echocardiographic sequences:active contours guided by optical flow estimates. IEEE Transactions on Medical Imaging,1998,17(2):274-284P
[169]Andrea G. On-line analysis of echocardiographic image sequences. Medical Image Analysis.1998,2(3):261-284P
[170]Ayache N, Cohen I, Herlin I. Medical image tracking. Active Vision, Blake A and Yuille A, Eds. Cambridge, MA:MIT Press,1992:285-302P
[171]徐俊红.基于光流的视频运动检测.哈尔滨工程大学硕士论文,2005.
[172]Xu C. Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images. A dissertation submitted to the Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy, Baltimore Maryland,1999 Jan.:10-22P
[173]Dang H, Hong Y, Fang X, Qiang F. Initial contour automatic selection of geometric active contour model.2009 Second International Conference on Intelligent Computation Technology and Automation,2009:66-69P
[174]徐翠,郑颖,王增福.一种基于图像底层特征的隐马尔科夫人体检测方法.模式识别与人工智能.2009,22(5)：743-749页
[175]Jia Y, Qu W. Real-time integrated multi-object detection and tracking in video sequences using detection and mean shift based particle filters. Proceedings-2010IEEE 2nd Symposium on Web Society, SWS 2010:738-743P
[176]Min W, Zhou Y J, Wan M X. A fast snake model based on non-linear diffusion for medical image segmentation. Computerized Medical Imaging and Graphics, 2004,28:109-117P
[177]Liu T W, Zhou H Y, Lin F Q, Pang Y S, Wu J. Improving image segmentation by gradient vector flow and mean shift. Pattern Recognition Letters.2008,29:90-95P
[178]田丽丽,郭敏,徐秋平.基于图割与GVF Snake的凹型目标快速提取算法.计算机应用.2008,28(10)：3633-2635页
[179]Xu J H, Wang C, Jin L. Gait recognition based on key frame and elliptical model. Proceedings of the 2010 IEEE, ICIA2010, June 2010, Harbin China:2483-2487P
[180]Zhao G, Chen R, Liu G, Li H. Amplitude spectrum-based gait recognition. Proceedings-Sixth IEEE International Conference on Automatic Face and Gesture Recognition,2004:23-28 P
[181]Collins R T, Gross R, Shi J. Silhouette-based human identification from body shape and gait. International Conference on Automatic Face and Gesture Recognition,2002: 351-356P
[182]Dockstader S L, Tekalp A M. A kinematic model for human motion and gait analysis. In Proceeding of the Workshop on Statistical Methods in Video Processing (ECCV), Copenhagen, Denmark, June 2002:49-54P
[183]Urtasun R. Fua P.3D tracking for gait characterization and recognition. Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR2004,2004:17-22P
[184]Zhao G, Liu G, Li H, Pietikainen M.3D gait recognition using multiple cameras. Proceedings of th7 International Conference on Automatic Face and Gesture Recognition. 2006:529-534P
[185]Wagg D K, Nixon M S. On automated model-based extraction and analysis of gait. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, May 2004:11-16P
[186]Lee Y, Jung K. Non-temporal multiple silhouettes in hidden markov model for view independent posture recognition.2009 International Conference on Computer Engineering and Technology.2009:466-470P
[187]Dong M, Zhang C, Bai Y R, Wan B K, Hu Y, Luk K D K. Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model.2009 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems, VECIMS 2009-Proceedings,2009:246-249P
[188]Phillips P. J, Sarkar S, Robledo I, Grother P, Bowyer K W. Baseline results for the challenge problem of human ID using gait analysis. Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC,USA,2002:130-135P
[189]Bai Y R, Zhang C, Wan B K, Hu Y, Luk K D K. Novel gait recognition technique based on SVM fusion of PCA-processed contour projection and skeleton model features. IEEE Conference on Computational Intelligence for Measurement Systems and Applications.2009, CIMSA'09:1-4P
[190]Wang L, Tan T, Ning H, Hu W. Silhouette analysis-based gait recognition for human identification. IEEE Transaction on Pattern Analysis and Machine Intelligence.2003, 25(12):1505-1518P
[191]Caselles V, Catte F, Coll T, Dibos F. A geometric model for active contours in image processing. Numerische Mathematik.1993,66(1):1-31P
[192]Xianye Ben, Weixiao Meng, Rui Yan. Dual-ellipse fitting approach for robust gait periodicity detection. Neurocomputing,2012,79(3):173-178
[193]Ahn S J, Rauh W, Cho H S, Wamecke H. Orthogonal distance fitting of implicit curves and surfaces. IEEE Transactions on pattern analysis and machine intelligence. 2002,24(5):620-638P
[194]Little J, Boyd J. Describing motion for recognition. Proceedings International Symposium on Computer Vision.1995, Coral Gables, FL, USA,235-240P
[195]Pentland A, Horowitz B. Recovery of nonrigid motion and structure. PAMI, July 1991,13(7):730-742P
[196]Meyer D. Human gait classification based on hidden Markov models.3D Image Analysis and Synthesis, Sankt Augustin:Infix,Germany,1997:139-146P
[197]Little J, Boyd J E. Recognizing people by their gait:the shape of motion. Journal of Computer Vision Research.1998,1(2):1-32P
[198]Huang P S, Harris C J, Nixon M S. Human gait recognition in canonical space using temporal templates. IEE Proc.-Vis. Image Signal Process.1999,146(2):93-100P
[199]Sun X Y, Zhang Q H, Xu Y Q. Application of segmentation based on optical flow for gait recognition], ICACTE, Chengdu, China,2010:567-571P
[200]Bashir K, Xiang T, Gong S G. Gait representation using flow fields. Proceedings of the British Machine Vision Conference, London, UK,2009:1-9P
[201]Han J, Bhanu B. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(2):316-322P
[202]张煜东,霍元铠,吴乐南,董正超.降维技术与方法综述.四川兵工学报.2010,31(10)：1-7页
[203]Cover T M, Thomas J A. Elements of information theory, Wiley Series in Telecommunications. John Wiley&Sons, NewYork, London, Sydnye,1991.
[204]Pang Y W, Zhang L, Liu Z K, Yu N H, Li H G. Neighborhood preserving projections (NPP):A novel linear dimension reduction method. Advances in intelligent computing: International Conference on Intelligent Computing,2005:117-125 P
[205]He X, Niyogi P. Locality preserving projections. Advances in Neural Information Processing Systems, Cambridge:MIT Press,2003:291-299P
[206]Borg I, Groenen P. Modern multidimensional scaling:theory and application. New York:Springer-Verlag,1997.
[207]Zha H Y, Zhang Z Y. Isometric embedding and continuum ISOMAP, CSE-03-001. Technical Report, CSE, Penn State Univ.2003.
[208]Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science,2000,290(5500):2319-2323P
[209]Bengio Y, Paiemen J F, Vincent P, Delalleau O, Roux N L, Ouimet M. Out-of sample extensions for LLE, Isomap, MDS, Eigenmaps and spectral clustering, Technical Report 1238, Department Informatique et Recherche Operationnelle, July 25,2003.
[210]Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems. Cambridge:MIT Press, 2001:585-591P
[211]Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science,2000,290 (5000),2323-2326P
[212]谭璐.高维数据的降维理论及应用.长沙,中国科学技术大学,2005.
[213]Seung H S, Lee D D. The manifold ways of perception. Science,2000,290 (5500): 2268-2269P
[214]谭璐,吴翊,易东云.稳健局部线性嵌入方法.国防科技大学学报.2004,26(6)：91-95页
[215]曹顺茂,叶世伟.一种改进的局部线性嵌入算法.计算机仿真.2007,24(5)：87-90页
[216]侯越先,吴静怡,何丕廉.基于局域主方向重构的适应性非线性维数约减.计算机应用.2006,26(4)：895-897页
[217]惠康华,肖柏华,王春恒.基于自适应近邻参数的局部线性嵌入.模式识别与人工智能,2010,23(6)：842-846页
[218]文贵华,包丽,丁月华.局部线性嵌入算法中参数的选取.计算机应用研究.2007,(2)：60-62页
[219]Kouropteva O, Okun O, Pietik "ainen M. Selection of the optimal parameter value for the locally linear embedding algorithm[C], In Proc. Of the 1st International Conference on Fuzzy Systems and Knowledge Discovery, Singapore,2002:359-363P
[220]郑杰.一种改进的LLE方法.湖南理工学院学报(自然科学版),2007,20(3)：30-32P
[221]黄鸿,李见为,冯海亮.基于有监督的核局部线性嵌入的人脸性别识别.光电子·激光,2009,20(2)：248-251页
[222]王和勇,郑杰,姚正安,李磊.基于聚类和改进距离的LLE方法在数据降维中的应用.计算机研究与发展,2006,43(8)：1485-1490页
[223]Saul L K, Roweis S. Think globally, fit locally:unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research,2003,4:119-155P
[224]Wu F C, Hu Z Y. The LLE and a linear mapping. Pattern Recognition,2006,39: 1799-1804 P
[225]Zhou C Y, Chen Y Q. Improving nearest neighbor classification with cam weighted distance. Pattern Recognition,2006,39:1-11 P
[226]Pan Y, Ge S S, Mamun A A. Weighted locally linear embedding for dimension reduction. Pattern Recognition.2009,42:798-811P
[2]朱珍,王景艳.生物识别技术与应用.佛山科学技术学院学报(自然科学版).2003,21(3)：66-69P
[3]Jain A K, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology.2004,14(1):4-20P
[4]Stroica P, Vladescu M. An overview on biometric identification methods and implementations. Proceding of SPIE.2010,7821:78211T-1-78211T-4P
[5]Boyd J E, Little J J. Biometric gait recognition. Advanced Studies in Biometrics. 2003.19-42P
[6]Johansson G. Visual perception of biological motion and a model for its analysis. Perception and Psychophysics 1973,14:201-211P
[7]Johansson G. Visual motion perception. Scientific American,1975:76-88P
[8]Cohen L, Shipley T F, Marshark E, Taht K, Aster D. Detecting animals in point-light displays. In:Twenty Second Annual Meeting of the Cognitive Science Society, Philadelphia, PA.2000:70P
[9]Cutting J E, Kozlowski L T. Recognizing friends by their walk:gait perception without familiarity cues. Bulletin of the Psychonomic Society.1977,9:353-356P
[10]Niyogi S A, Adelson E H. Analyzing and recognizing walking figures in XYT. IEEE Conference on Computer Vision and Pattern Recognition.1994:469-474P
[11]Nixon M S, Carter J N, Nash J M, Huang P S, Cunado D, Stevenage S V. Automatic gait recognition. Kluwer Academic Publishers.1999:231-250P
[12]Rohila N, Kumar B, Chauhan N. Abnormal gait recognition. International Journal on Computer Science and Engineering.2010,2(5):1544-1551P
[13]Larsen P K, Simonsen E B, Lynnerup N. Gait analysis in forensic medicine. Journal of Forensic Sciences.2008,53:1149-1153P
[14]Carlo F, Paolo C. Multichannel SEMG in clinical gait analysis:A review and state-of-the-art. Clinical Biomechanics.2009,24(3):236-245P
[15]Leu A, Ristic-Durrant D, Graser A. A robust markerless vision-based human gait analysis system.6th IEEE International Symposium on Applied Computational Intelligence and Informatics,2011, Timisoara, Romania:415-420P
[16]Nixon M S, Carter J N. Automatic recognition by gait. Proc. IEEE.2006, 94(11):2013-2024P
[17]Haritaoglu I, Cutler R, Harwood D, Davis L S. Backpack:detection of people carrying objects using silhouettes. Computer Vision and Image Understanding.2001,81(3): 385-397P
[18]Boulgouris N V, Hatzinakos D, Plataniotis K N. Gait recognition:a challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine, 2005,22(6):78-90P
[19]Li Y B, Jiang T X, Qiao Z H, Qian H J. General methods and development actuality of gait recognition. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China.2007:2-4P
[20]Gafurov D. A survey of biometric gait recognition:approaches, security and challenges. in Proceedings of Annual Norwegian Computer Science Conference,2007, http://www.iu.hio.no/nik07/bidrag/Gafurov.pdf.
[21]陈昌红,梁继民,赵恒,焦李成.步态表征和步态融合方法新进展.计算机科学.2010,37(8)：15-20页
[22]Wang J, She M, Nahavandi S. A review of vision-based gait recognition methods for human identification[C], IEEE Proceedings of the Digital Image Computing:Techniques and Application, Piscataway, N.J,2010, pp:320-327.
[23]Nixon M. S, Carter J N, Shutler J, Grant M. New advances in automatic gait recognition. Elsevier Information Security Technical Report,2002,7 (4):23-35P
[24]Zhang Z X, Hu M D, Wang Y H. A survey of advances in biometric gait recognition. Lecture Notes in Computer Science:Biometric Recognition.2011,7098:150-158P
[25]贲睍烨,徐森,王科俊.行人步态的特征表达及识别综述.模式识别与人工智能,2012,25(1)：71-81
[26]Haritaoglu I, Davis L S, Harwood D. W4 who? when? where? what? a real time system for detecting and tracking people. In:Proc. IEEE International Conference on Automatic Face and Gesture Recognition.1998:222-227P
[27]Long W, Yang Y. Stationary background generation:An alternative to the difference of two images. Pattern Recognition.1990,23(12):1351-1359P
[28]Kornprobst P, Deriche R. Aubert G. Image sequence analysis via partial difference equations. Journal of Mathematical Imaging and Vision.1999.11(1):5-26P
[29]Rtlssell D, Gong S. A highly efficient block-based dynamic background model. Proc of the IEEE Conference on advanced Video and Signal Based Surveillance. London. UK, 2005:417-422P
[30]杨广林,孔令富.基于图像分映的背景模型构建方法.机器人.2007,29(1)：29-34P
[31]Heikkila M, Pietikainen M. A texture based method for modeling the background and detecting moving objects. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2006,28(4):657-662P
[32]Halevy G, Weinshall D. Motion of disturbances:detection and tracking of multi-body non-rigid motion. Machine Vision and Applications.1999,11(3):122-137P
[33]Elgammal A, Duraiswami R, Davis L. Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence.2003,25(11):1499-1504P
[34]Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection. IEEE Transaction on Pattern Recognition and Machine Intelligence.2005,27(11):1778-1792P
[35]Power P W, Schoonees J A. Understanding background mixture models for foreground segmentation. In:Proc. Image and Vision Computing, Auckland, New Zealand, 2002:267-271 P
[36]Lee D S, Hull J, ErolB. A bayesian framework for gaussian mixture background modeling. In:Proc. IEEE International Conference on Image Processing,2003:973-976P
[37]Dar-shyang Lee. Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(5):827-832P
[38]KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. In:Proc. European Workshop Advanced Video Based Surveillance Systems.2001,1(3):1-5P
[39]Cheng J. Flexible background mixture models for foreground segmentation. Image and Vision Computing.2006,24:473-482P
[40]Rittseher J, Kato J, Joga S. A probabilistic background model for tracking. Proc of the 6th European Conference on Computer Vision. Hilton Head Island, USA,2000, Ⅱ: 255-261P
[41]Prati A, Mikic I, Trivedi M. Detecting moving shadows:formulation, algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(7): 918-924P
[42]Bai S C, Yun Q L. Indoor and outdoor people detection and shadow suppression by exploiting HSV color information. In:Proc. the 4th International Conference on Computer and Information Technology. Wuhan, China,2004:137-142P
[431 Leone A, Distante C, Buccolieri F. A texture-based approach for shadow detection. IEEE Conference on Advanced Video and Signal Based Surveillance.2005:371-376P
[44]Stauder J, Mech R, Ostermann J. Detection of moving cast shadows for object segmentation. IEEE Transactions on Multimedia.1999,1(1):65-76P
[45]Martel-Brisson N, Zaccarin A. Learning and removing cast shadows through a multidistribution approach. IEEE Transactions on Pattern Analysis and Machine Intelligence.2007,29(7):1133-1146P
[46]王智灵,周路平,陈宗海.针对不同信息特征的鲁棒背景建模技术分析.模式识别与人工智能.2009,22(2)：240-245P
[47]丁亮,张永平,张雪英.图像分割方法及性能评价综述.2010,31(12)：78-83P
[48]Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision. In:Proc. the 7th International Joint Conference on Artificial Intelligence, Vancouver,1981:674-679P
[49]Horn B K P, Schunck B G. Determining optical flow. Artificial Intelligence.1981, 17(1):185-204P
[50]Wren C. R, Azarbayejani A, Darrell T. Pfinder:real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997,19(7):780-785P
[51]Masoud O, Papanikolopoulos N P. A novel method for tracking and counting pedestrians in real time using a single camera. IEEE Transactions on Vehicular Technology.2001, 50(5):1267-1278P
[52]Dockstader S L, Tekalp A M. Multiple cameras tracking of interacting and occluded human motion. Proceedings of the IEEE.2001,89(10):1441-1455P
[53]McKenna S, Jabri S, Duric Z. Tracking groups of people. Computer Vision and Image Understanding.2000,80(1):42-56P
[54]Zhao T, Nevatia R. Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9),pp:1208-1221.
[55]Bar-Shalom Y, Fortmann T. Tracking and data association. Academic Press,1988.
[56]王勇.基于统计方法的运动目标检测与跟踪技术研究.华中科技大学博士学位.2009：11-18页
[57]Jang D S, Choi H I. Active models for tracking moving objects. Pattern Recognition. 2000,33(7):1135-1146P
[58]Har H I K, Schonfeld D. Fast object tracking using adaptive block matching. IEEE Transactions on Multimedia.2005,7 (5):853-859P
[59]Wren C R, Azarbayejan I A, Darrel L, Pentland A P. Pfinder:real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997.19 (7):780-785P
[60]Lu J, Hiroyasu I, KYoko H. A hybrid vision method for autonomous guided vehicle navigation. ACTA Automatic Sinica.2006,32(6):988-998P
[61]Birchfield S. Elliptical head tracking using intensity gradients and color histograms. Proc of IEEE Conference on Computer Vision and Pattern Recognition.1998:232-237P
[62]Dutta A, Kar A, Chatterji B N. Corner detection algorithms for digital images in last three decades. IETE Technical Review.2008,25(3):123-133P
[63]Chen Z, Birchfild S T. Person following with a mobile robot using binocular feature-based tracking. IEEE/RSJ International Conference on Intelligence Robots and Systems (IROS 2007). San Diego,2007:815-820P
[64]杨育彬,林珲.利用天文观测图像对空间碎片目标进行自动识别与追踪.武汉大学学报(信息科学版).2010,35(2)：209-214P
[65]Yokoyama M, Poggio T. A contour-based moving object detection and tracking.2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking,2005.
[66]Jonas D V, Daniel O, Wilfried P, Laury C, Dominique V D S. Tracking multiple objects using moving snakes.16th International Conference on Digital Signal Processing, Proceedings, IEEE Computer Society,445 Hoes Lane-P.O.Box 1331, Piscataway, USA, 2009
[67]杨涛,李静,潘泉,张艳宁.一种基于贪心搜索的实时多目标遮挡处理算法.自动化学报.2010,36(3)：375-384页
[68]Smith C E. Fast tracking of natural textures using fractal snakes.2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010, Istanbul, Turkey,2010:2175-2181P
[69]Wang C, Dai G. Moving targets detection and tracking based on bayesian foreground segmentation and GVF-snake.3rd International Workshop on Advanced Computational Intelligence, IWACI 2010. Suzhou, China,2010:565-569P
[70]Aksel A, Acton S. Target tracking using the snake particle filter. Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation. Institute of Electrical and Electronics Engineers Inc., Austin, USA,2010:33-36P
[71]董春利,董育宁,刘杰.基于粒子滤波和GVF Snake的目标跟踪算.仪器仪表学报.2009,30(4)：828-833页
[72]Chen Z, Lee H J. Knowledge-guided visual perception of 3D human gait from a single image sequence. IEEE Transactions on Systems, Man and Cybernetics. 1992,22(2):336-343P
[73]Ju S X, Black MJ, Yacoob Y. Cardboard people:a parameterized model of articulated image motion. Proc of the 2nd IEEE International Conference on Automatic Face and Gesture Recognition. Washington DC:IEEE Computer Society,1996:38-44页
[74]Wachter S, Nageltt H H. Tracking of persons in monocular image sequences. Computer Vision on Image Understanding.1999,74(3):174-192P
[75]Bergh M, Koller-meier E, Gool L. Real-time body pose recognition using 2D or 3D haarlets. International Journal of Computer Vision 2009,83(1):72-84P
[76]Zhang R, Vogler C, Metaxas D. Human gait recognition. IEEE Conference on Computer Vision and Pattern Recognition.2004:18-25P
[77]杨军,吴晓娟,彭彰,陈文刚.基于多区域分割的步态表示与识别算法研究.计算机学报.2006,29(10)：1876-1881页
[78]Lee L, Grimson W. Gait analysis for recognition and classification. International Conference on Automatic Face and Gesture Recognition.2002:155-162P
[79]Bhanu B, Han J. Individual recognition by kinematic-based gait analysis.16th Proceedings of International Conference on Pattern Recognition. Quebec, Canada,2002,3: 343-346P
[80]Kaziska, D, Srivastava A. Joint gait-cadence analysis for human identification using an elastic shape framework. Communications in Statistics-Theory and Methods.2010, 39(10):1817-1831P
[81]Zhao G, Liu G, Li H, Pietikainen M.3D gait recognition using multiple camera. International Conference on Automatic Face and Gesture Recognition.2006:529-534P
[82]Nash J M, Carter J N, Nixon M S. Dynamic feature extraction via the velocity hough transform. Pattern Recognition Letters.1997,18(10):1035-1047.
[83]Su S Z, Wang L, Li S Z. Interframe variation vector:a novel feature for gait recognition. Journal of Donghua University (English Edition).2010,27(2):233-236P
[84]Li Y, Yang Q. Gait recognition algorithm based on the tiptoe trajectory. Journal of Computational Information Systems.2010,6(13):4493-4502P
[85]Kim D, Paik J. Gait recognition using active shape model and motion prediction. IET Computer Vision.2010,4(1):25-36P
[86]贾楷熙,薛静.基于Zernike矩和BP网络的步态识别技术研究.西北工业大学学学报.2010,28(5)：669-673页
[87]Han S. Liao Z W, Chen G Y. A gait recognition method using L1-PCA and LDA. Proceedings of the Eighth International Conference on Machine Learning and Cybernetics. Baoding,2009:3198-3200P
[88]Han J, Bhanu B. Statistical feature fusion for gait-based human recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA,2004:842-847P
[89]Liu J, Zheng N. Gait history image:a novel temporal template for gait recognition. IEEE International Conference on Multimedia and Expo. Xi-an, China,2007:663-666P
[90]Lam T H W, Cheung K H, Liu J N K. Gait flow image:A silhouette-based gait representation for human identification. Pattern Recognition.2011,44(4):973-987P
[91]Xu S L, Zhang Q J. Gait recognition using fuzzy principal component analysis.2nd International Conference on e-Business and Information System Security, EBISS. Wuhan, China,2010:10-13P
[92]Lee S, Liu Y, Collins R. Shape variation-based frieze pattern for robust gait recognition. IEEE International Conference on Computer Vision and Pattern Recognition. Minneapolis, MN,2007:1-8P
[93]Chen C H, Liang J, Zhao H, Hu H, Tian J. Factorial HMM and parallel HMM for gait recognition. IEEE Transactions on Systems, Man and Cybernetics, Part C.2009,39(1): 114-123P
[94]Kusakunniran W, Wu Q. Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron. Pattern Recognition Letters.2011, In Press.
[95]Goffredo M, Bouchrika I, Carter J N, Nixon M S. Self-calibrating view-invariant gait biometrics. IEEE Transactions on Systems, Man and Cybernetics, Part B.2010, 40(4):997-1008P
[96]Goffredo M, Bouchrika I, Carter J, Nixon M. Performance analysis for automated gait extraction and recognition in multi-camera surveillance. Multimedia Tools and Applications.2010,50(1):75-94P
[97]Su H, Chen G Y. A new method of gait recognition independent of view angle. Proceedings of the Ninth International Conference on Machine Learning and Cybernetics. Qingdao,2010:3091-3093P
[98]Liu H, Cao Y, Wang Z. A novel algorithm of gait recognition. International Conference on Wireless Communication& Signal Processing,2009:1-5P
[99]Kusakunniran W, Wu Q, Zhang J, Li H. Support vector regression for multi-view gait recognition based on local motion feature selection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco,2010:974-981P
[100]Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y. Gait recognition using a view transformation model in the frequency domain. In Europeon Conference on Computer Vision.2006:151-163P
[101]Lu J, Tan Y P. Uncorrelated discriminant simplex analysis for view-invariant gait signal computing. Pattern Recognition Letters,2010,31(5):382-393P
[102]Gu J, Ding X, Wang S, Wu Y. Action and gait recognition from recovered 3-D human joints. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, 2010,40(4):1021-1033P
[103]Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N. View-independent human motion classification using image-based reconstruction. Image and Vision Computing.2009,27(8):1194-1206P
[104]Liu N, Lu J, Tan Y P. Joint subspace learning for view-Invariant gait recognition. IEEE Signal Processing Letters.2011,18(7):431-434P
[105]Wang L, Ning H, Tan T. Fusion of static and dynamic body biometrics for gait recognition. IEEE Transactions on oil Circuit and Systems for Video Technology.2004, 15(2):149-158P
[106]Chai Y, Wang Q, Jia J. A novel gait recognition method via fusing shape and kinematics features. International Conference on Multimedia and Expo. Springer Berlin,2006: 80-89P
[107]Jain A, Dass S, Nandakumar K. Can soft biometric traits assist user recognition? Proceedings of SPIE.2004,5404:561-572P
[108]Moustakas K, Tzovaras D. Gait recognition using geometric features and soft biometrics. IEEE Signal Processing Letters.2010,17(4):367-370P
[109]Yun J. User identification using gait patterns on UbiFloorⅡ. Sensors.2011,11(3): 2611-2639P
[110]徐苏.基于步态触觉特征的生物特征识别.中国科学技术大学博士论文集.2010.
[111]李轶,明东,王璐.綦宏志,万柏坤.融合步态和人脸特征的远距离身份识别研究.仪器仪表学报.2011,32(2)：264-270页
[112]Geng X, Smith-Miles K. Context-aware fusion:A case study on fusion of gait and face for human identification in video. Pattern Recognition.2010,43(10):3660-3673P
[113]Zhao X, Du Y. Research on gait-based human identification. ICCASM, Shanxi, Taiyuan.2010:12480-12482P
[114]Zhang J, Liu Z. Real-time gait classification based on fuzzy associative memory. International Journal of Modelling, Identification and Control.2010,10(3-4):263-271P
[115]Aravind S, RoyChowdhuiy A, Chellappa R. A hidden markov model based framework for recognition of humans from gait sequences. International Conference on Image Processing.2003,2:93-96P
[116]Nizami I. F, Hong S. Automatic gait recognition based on probabilistic approach. International Journal of Imaging Systems and Technology.2010,20(4):400-408P
[117]Lee C S, Elgammal A. Style adaptive contour tracking of human gait using explicit manifold models. Machine Vision and Applications.2010:1-18P
[118]李颖佩,卢结成,徐玉辉.基于Zernike矩快速算法的步态识别.计算机仿真.2010,27(11)：238-241页
[119]Moon H, Phillips P J. Computational and performance aspects of PCA-based face-recognition algorithms. Perception.2001,30:303-321P
[120]Phillips P J, Moon H, Rizvi S A, Rauss P J. The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern analysis and Machine Intelligence.2000,22(10):1090-1104P
[121]郑南宁.计算机视觉与模式识别,国防工业出版社.1998：192-198页
[122]邵文坤,黄爱民,韦庆.动态场景下的运动目标跟踪方法研究.计算机仿真.2006,23(5)：181-184页
[123]Gibson J J. The perception of the visual world. Houghton Mifflin Publishers, Boston. 1950:25-28P
[124]章毓晋编著.图像工程(上册).北京：清华大学出版社,1999：67-71页
[125]Beauchemin S S, Barron J L. The computation of optical flow. ACM Computing.1995, 27(3):433-466P
[126]Fazekas S, Chetverikov D. Dynamic texture recognition using optical flow features and temporal periodicity. Content-Based Multimedia Indexing,2007:25-32P
[127]Quan H, Wang C. Overview of dynamic texture recognition techqiques based on optical flow. International Conference on Computational Intelligence and Software Engineering. 2009:1-4P
[128]Anandan P. A computational framework and algorithm for the measurement of visual motion. International Journal of Computer Vision.1989,2(3):283-310P
[129]Fleet D J, Jepson A D, Jenkin M R M. Phase-based disparity measurement. CVGIP: Image Understanding.1991,53(2):198-210P
[130]Singh A. Optic flow computation:a unified perspective. Piscataway, NJ:IEEE Computer Society Press,1992.
[131]Weber J, Malik J. Robust computation of optical flow in a multi-scale differential framework. International Journal of Computer Vision.1995,14:67-81P
[132]Gastounioti A, Golemati S, Tsiaparas N N, Stoitsis J S, Nikita K S. Multiscale approach for weighted least-squares optical flow for estimating arterial wall displacements.2011 10th International Workshop on Biomedical Engineering, Kos Island, Greece.2011:1-4P
[133]Brox T, Malik J. Large displacement optical flow:descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence.2011, 33(3):500-513P
[134]Ince S, Konrad J. Occlusion-aware optical flow estimation. IEEE Transactions on Image Precessing.2008,17(8):1443-1451P
[135]Seegmiller N, Wettergreen D. Optical flow odometry with robustness to self-shadowing. International Conference on Intelligent Robots and Systems,2011, San Francisco, California:613-618P
[136]Itohara T, Otsuka T, Mizumoto T, Ogata T, Okuno H. G. Particle-filter based audio-visual beat-tracking for music robot ensemble with human guitaris. International Conference on Intelligent Robots and Systems,2011, San Francisco, California:118-124P
[137]Song X, Seneviratne L.D, Althoefer K. A kalman filter-integrated optical flow method for velocity sensing of mobile robots. IEEE/ASME Transactions on Mechatronics, 2011,16(3),pp:551-563.
[138]Schmudderich J, Willert V, Eggert J, Rebhan S, Goerick C, Sagerer G, Korner E. Estimating object proper motion using optical flow, kinematics, and depth information. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics.2008, 38(4):1139-1151P
[139]姜浩,郁道银,孙正,徐智,黄家祥.光流法在冠状动脉运动分析中的应用.光电子激光.2004,15(9)：1128-1131页
[140]Tanaka A, Yoshifumi S. Blood flow visualization of left atrial spontaneous echo contrast(SEC) using gradient based optical flow estimation. Proceedings of the 29th Annual International Conference of the IEEE EMBS cite International, Lyon, France. 2007:4500-4503P
[141]Burlina p, Hoffmann B, Abraham T. Computing lest ventricular hemodynamics from echographic optical flow of CEUS microspheres. Life Science Systems and Applications Workshop.2011:67-72P
[142]Danilouchkine M, Mastik F, vander S A. Improving IVUS palpography by incorporation of motion compensation based on block matching and optical flow. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.2008, 55(11):2392-2404P
[143]Dawood M, Buther F, Jiang X, Schafers K P. Respiratory motion correction in 3-D PET data with advanced optical flow algorithms. IEEE Transactions on Medical Imaging. 2008,27(8):1164-1175P
[144]Mitiche A, Sekkati. H. Optical flow 3D segmentation and interpretation:a variational method with active vurve evolution and level sets. IEEE Tansactions on Pattern Analysis and Machine Intelligence.2006,28:1818-1829P
[145]Schwarz L A, Mkhitaryan A, Mateus D, Navab N. Estimating human 3D pose from time-of-flight images based on geodesic distances and optical flow. IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, Santa Barbara, California on March 21-25,2011:700-706P
[146]Romero H, Salazar S, Lozano R. Real-time stabilization of an eight-rotor UAV using optical flow. IEEE Transactions on Robotics.2009,25(4):808-817P
[147]Michalke T P, Stein F, Franke U. Towards a closer fusion of active and passive safety: Optical flow-based detection of vehicle side collisions. Intelligent Vehicles Symposium (IV),2011 IEEE, Alcala de Henares, Spain.2011:181-188P
[148]Black M J, Anandan P. The robust estimation of multiple motions:parametic and piece wise-smooth flow fields. Compufer vision and image understanding.1996,63(1): 75-104P
[149]Liao S H, Liu B Y. An edge-based approach to improve optical flow algorithm[C],3rd International Conference on Advanced Computer Theory and Engineering,2010,6: 45-51P
[150]Manchester I R, Savkin A V, Faruqi F A. Method for optical-flow-based precision missile guidance. IEEE Transactions on Aerospace and Electronic Systems. 2008,4(3):835-851P
[151]Luo R C, Huang C Y, Lin P H. Alignment and tracking of facial features with component-based active appearance models and optical flow. Advanced Intelligent Mechatronics (AIM),2011 IEEE/ASME International Conference on, Budapest, Hungary, 2011:1058-1063P
[152]Hsieh C K, Chen Y C. An optical flow-based approach to robust face recognition under expression variations. IEEE Transactions on Image Processing.2009,19(1):233-240 P
[153]Wang J Y A, Adelson E H. Representing moving images with layers, IEEE Transactions on Image Process.1994,3(5):625-638P
[154]焦春林,高满屯,曹秋萍,陈淑梅.基于光流场的图像分割.西北工业大学学报. 2006,24(2)：265-269页
[155]Horn B K P. Robot Vision]. Cambridge, MA:MIT Press,1986:165-178P
[156]Jain A K. Fundamentals of digital image processing, Englewood Cliffs, NJ: Prentice-Hall,1989:311-315P
[157]Amini A A, Tenran S, Weymouth T E. Using dynamic programming for minimizing the energy of active contours in the presence of hard constrains. Proceeding of the 2nd International Conference of Computer Vision.1988:95-99P
[158]黄志彦,曹春红,徐剑.基于小波多尺度分析与GVF Snake的空中目标提取.光电与控制.2002,9(4)：33-36页
[159]王立功,于甬华,姜晓彤,罗立民.基于Snake模型的图像目标轮廓自动跟踪方法.东南大学学报(自然科学版).2003,33(2)：215-218页
[160]罗希平,田捷.一种改进的交互式医学图像序列分割方法.电子学报.2003,31(1)：29-32页
[161]郭礼华,李建华,杨树唐.基于运动补偿的Snake视频对象跟踪算法.红外与激光工程.2005,34(1)：93-97页
[162]邱书博,王化祥,梁志伟.一种新的B-Snake算法在目标轮廓跟踪中的应用.中国图象图形学报.2005,10(5)：585-589页
[163]Wren C R, Azarbayejani A, Darrell T, Pentland A P. Pfinder:real-time tracking of human body. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997, 19(7):780-785P
[164]Dong C L, Dong Y N. A novel algorithm for object tracking with particle filtering and GVF snake. Eighth International Conference on Intelligent Systems Design and Applications.2008:8-13P
[165]Rathi Y, Vaswani N, Tannenbaum A. Tracking deformable objects using particle filtering for geometric active contours. IEEE Trans. Pattern Analysis and Machine Intelligence,2007,29(8):1470-1475P
[166]Betser A, Vela P, Tannenbaum A. Automatic tracking of flying vehicles using geodesic snakes and Kalman filtering.43rd IEEE Conference on Decision and Control.2004,2: 1649-1654P
[167]Khansari M, Rabiee H R, Asadi M. A robust object shape prediction algorithm in the presence of White Gaussian Noise. Proceedings of 12th International Multi-Media Modelling Conference.2006,1:4-6P
[168]Ivana M, Slawomir K, James D T. Segmentation and tracking in echocardiographic sequences:active contours guided by optical flow estimates. IEEE Transactions on Medical Imaging,1998,17(2):274-284P
[169]Andrea G. On-line analysis of echocardiographic image sequences. Medical Image Analysis.1998,2(3):261-284P
[170]Ayache N, Cohen I, Herlin I. Medical image tracking. Active Vision, Blake A and Yuille A, Eds. Cambridge, MA:MIT Press,1992:285-302P
[171]徐俊红.基于光流的视频运动检测.哈尔滨工程大学硕士论文,2005.
[172]Xu C. Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images. A dissertation submitted to the Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy, Baltimore Maryland,1999 Jan.:10-22P
[173]Dang H, Hong Y, Fang X, Qiang F. Initial contour automatic selection of geometric active contour model.2009 Second International Conference on Intelligent Computation Technology and Automation,2009:66-69P
[174]徐翠,郑颖,王增福.一种基于图像底层特征的隐马尔科夫人体检测方法.模式识别与人工智能.2009,22(5)：743-749页
[175]Jia Y, Qu W. Real-time integrated multi-object detection and tracking in video sequences using detection and mean shift based particle filters. Proceedings-2010IEEE 2nd Symposium on Web Society, SWS 2010:738-743P
[176]Min W, Zhou Y J, Wan M X. A fast snake model based on non-linear diffusion for medical image segmentation. Computerized Medical Imaging and Graphics, 2004,28:109-117P
[177]Liu T W, Zhou H Y, Lin F Q, Pang Y S, Wu J. Improving image segmentation by gradient vector flow and mean shift. Pattern Recognition Letters.2008,29:90-95P
[178]田丽丽,郭敏,徐秋平.基于图割与GVF Snake的凹型目标快速提取算法.计算机应用.2008,28(10)：3633-2635页
[179]Xu J H, Wang C, Jin L. Gait recognition based on key frame and elliptical model. Proceedings of the 2010 IEEE, ICIA2010, June 2010, Harbin China:2483-2487P
[180]Zhao G, Chen R, Liu G, Li H. Amplitude spectrum-based gait recognition. Proceedings-Sixth IEEE International Conference on Automatic Face and Gesture Recognition,2004:23-28 P
[181]Collins R T, Gross R, Shi J. Silhouette-based human identification from body shape and gait. International Conference on Automatic Face and Gesture Recognition,2002: 351-356P
[182]Dockstader S L, Tekalp A M. A kinematic model for human motion and gait analysis. In Proceeding of the Workshop on Statistical Methods in Video Processing (ECCV), Copenhagen, Denmark, June 2002:49-54P
[183]Urtasun R. Fua P.3D tracking for gait characterization and recognition. Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR2004,2004:17-22P
[184]Zhao G, Liu G, Li H, Pietikainen M.3D gait recognition using multiple cameras. Proceedings of th7 International Conference on Automatic Face and Gesture Recognition. 2006:529-534P
[185]Wagg D K, Nixon M S. On automated model-based extraction and analysis of gait. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, May 2004:11-16P
[186]Lee Y, Jung K. Non-temporal multiple silhouettes in hidden markov model for view independent posture recognition.2009 International Conference on Computer Engineering and Technology.2009:466-470P
[187]Dong M, Zhang C, Bai Y R, Wan B K, Hu Y, Luk K D K. Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model.2009 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems, VECIMS 2009-Proceedings,2009:246-249P
[188]Phillips P. J, Sarkar S, Robledo I, Grother P, Bowyer K W. Baseline results for the challenge problem of human ID using gait analysis. Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC,USA,2002:130-135P
[189]Bai Y R, Zhang C, Wan B K, Hu Y, Luk K D K. Novel gait recognition technique based on SVM fusion of PCA-processed contour projection and skeleton model features. IEEE Conference on Computational Intelligence for Measurement Systems and Applications.2009, CIMSA'09:1-4P
[190]Wang L, Tan T, Ning H, Hu W. Silhouette analysis-based gait recognition for human identification. IEEE Transaction on Pattern Analysis and Machine Intelligence.2003, 25(12):1505-1518P
[191]Caselles V, Catte F, Coll T, Dibos F. A geometric model for active contours in image processing. Numerische Mathematik.1993,66(1):1-31P
[192]Xianye Ben, Weixiao Meng, Rui Yan. Dual-ellipse fitting approach for robust gait periodicity detection. Neurocomputing,2012,79(3):173-178
[193]Ahn S J, Rauh W, Cho H S, Wamecke H. Orthogonal distance fitting of implicit curves and surfaces. IEEE Transactions on pattern analysis and machine intelligence. 2002,24(5):620-638P
[194]Little J, Boyd J. Describing motion for recognition. Proceedings International Symposium on Computer Vision.1995, Coral Gables, FL, USA,235-240P
[195]Pentland A, Horowitz B. Recovery of nonrigid motion and structure. PAMI, July 1991,13(7):730-742P
[196]Meyer D. Human gait classification based on hidden Markov models.3D Image Analysis and Synthesis, Sankt Augustin:Infix,Germany,1997:139-146P
[197]Little J, Boyd J E. Recognizing people by their gait:the shape of motion. Journal of Computer Vision Research.1998,1(2):1-32P
[198]Huang P S, Harris C J, Nixon M S. Human gait recognition in canonical space using temporal templates. IEE Proc.-Vis. Image Signal Process.1999,146(2):93-100P
[199]Sun X Y, Zhang Q H, Xu Y Q. Application of segmentation based on optical flow for gait recognition], ICACTE, Chengdu, China,2010:567-571P
[200]Bashir K, Xiang T, Gong S G. Gait representation using flow fields. Proceedings of the British Machine Vision Conference, London, UK,2009:1-9P
[201]Han J, Bhanu B. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(2):316-322P
[202]张煜东,霍元铠,吴乐南,董正超.降维技术与方法综述.四川兵工学报.2010,31(10)：1-7页
[203]Cover T M, Thomas J A. Elements of information theory, Wiley Series in Telecommunications. John Wiley&Sons, NewYork, London, Sydnye,1991.
[204]Pang Y W, Zhang L, Liu Z K, Yu N H, Li H G. Neighborhood preserving projections (NPP):A novel linear dimension reduction method. Advances in intelligent computing: International Conference on Intelligent Computing,2005:117-125 P
[205]He X, Niyogi P. Locality preserving projections. Advances in Neural Information Processing Systems, Cambridge:MIT Press,2003:291-299P
[206]Borg I, Groenen P. Modern multidimensional scaling:theory and application. New York:Springer-Verlag,1997.
[207]Zha H Y, Zhang Z Y. Isometric embedding and continuum ISOMAP, CSE-03-001. Technical Report, CSE, Penn State Univ.2003.
[208]Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science,2000,290(5500):2319-2323P
[209]Bengio Y, Paiemen J F, Vincent P, Delalleau O, Roux N L, Ouimet M. Out-of sample extensions for LLE, Isomap, MDS, Eigenmaps and spectral clustering, Technical Report 1238, Department Informatique et Recherche Operationnelle, July 25,2003.
[210]Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems. Cambridge:MIT Press, 2001:585-591P
[211]Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science,2000,290 (5000),2323-2326P
[212]谭璐.高维数据的降维理论及应用.长沙,中国科学技术大学,2005.
[213]Seung H S, Lee D D. The manifold ways of perception. Science,2000,290 (5500): 2268-2269P
[214]谭璐,吴翊,易东云.稳健局部线性嵌入方法.国防科技大学学报.2004,26(6)：91-95页
[215]曹顺茂,叶世伟.一种改进的局部线性嵌入算法.计算机仿真.2007,24(5)：87-90页
[216]侯越先,吴静怡,何丕廉.基于局域主方向重构的适应性非线性维数约减.计算机应用.2006,26(4)：895-897页
[217]惠康华,肖柏华,王春恒.基于自适应近邻参数的局部线性嵌入.模式识别与人工智能,2010,23(6)：842-846页
[218]文贵华,包丽,丁月华.局部线性嵌入算法中参数的选取.计算机应用研究.2007,(2)：60-62页
[219]Kouropteva O, Okun O, Pietik "ainen M. Selection of the optimal parameter value for the locally linear embedding algorithm[C], In Proc. Of the 1st International Conference on Fuzzy Systems and Knowledge Discovery, Singapore,2002:359-363P
[220]郑杰.一种改进的LLE方法.湖南理工学院学报(自然科学版),2007,20(3)：30-32P
[221]黄鸿,李见为,冯海亮.基于有监督的核局部线性嵌入的人脸性别识别.光电子·激光,2009,20(2)：248-251页
[222]王和勇,郑杰,姚正安,李磊.基于聚类和改进距离的LLE方法在数据降维中的应用.计算机研究与发展,2006,43(8)：1485-1490页
[223]Saul L K, Roweis S. Think globally, fit locally:unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research,2003,4:119-155P
[224]Wu F C, Hu Z Y. The LLE and a linear mapping. Pattern Recognition,2006,39: 1799-1804 P
[225]Zhou C Y, Chen Y Q. Improving nearest neighbor classification with cam weighted distance. Pattern Recognition,2006,39:1-11 P
[226]Pan Y, Ge S S, Mamun A A. Weighted locally linear embedding for dimension reduction. Pattern Recognition.2009,42:798-811P