基于统计方法的运动目标检测与跟踪技术研究
|
摘要
随着计算机科学、电子技术、自动控制以及人工智能的发展与普及，智能视频监控技术已被广泛应用到国民经济的各领域，它在军事、工业、智能人机交互、智能交通和科学研究等方面都具有重要的意义，应用前景广阔。作为智能视频监控的核心关键技术，序列图像目标检测与跟踪在理论和应用上仍存在许多问题和难点尚未解决。很多因素都会影响在序列图像中对运动目标的可靠观测，例如，视觉特征分辨力较弱、背景干扰、目标间相互遮挡，加上实际环境中目标运动的随机性和复杂性(目标大小、形变、运动速度、光照变化、目标颜色与背景颜色的相似程度等)，这些都使得算法的设计变得非常困难。因此，研究复杂场景下的目标检测与跟踪具有重要的理论意义和很高的实用价值。论文针对复杂场景中运动目标检测与跟踪中存在的若干问题，对运动目标的准确分割、区域特征提取、目标描述及鲁棒跟踪等关键技术进行了研究与探讨。论文的主要研究内容和成果概括如下：
(1)对静态背景下运动目标的检测与分割进行了研究。针对复杂背景、光照变化、阴影等影响目标检测的问题，提出了一种有效的自适应混合高斯背景建模算法，各像素点根据其像素值出现的混乱程度采取不同个数的高斯分布描述，重新设计了背景模型的学习与更新方法以及高斯分布生成准则；采用基于联合像素时空信息的分割与形态学重构方法使得前景目标分割的性能得到了有效地提高。同时文中也给出了阴影检测与抑制、光照突变的处理方法。该算法能够快速准确地建立背景模型，准确分割前景目标。
(2)针对固定监控场景提出了一种基于色彩分割与目标局部模型匹配的非刚性目标跟踪算法。利用自适应混合高斯背景模型提取前景运动目标，通过基于均值漂移滤波与区域生长的色彩分割算法建立目标局部模型并实时更新，结合区域约束条件和目标模型区域匹配实现目标的跟踪。算法能有效地解决跟踪过程中目标局部遮挡与形变问题。
(3)针对基于核的跟踪算法中颜色直方图对目标特征描述较弱、跟踪过程中核函数带宽保持不变、无法实现模板更新的缺点，提出了一种基于偏移校正的核空间直方图目标跟踪算法。在特定的色彩空间中，联合像素颜色与空间信息建立目标模型，即建立目标颜色直方图的同时用高斯分布对直方图各区间内的像素坐标建模，有效地解决了目标与背景颜色相似情况下的跟踪问题。通过目标特征点匹配估算目标仿射模型参数实现跟踪窗的更新。结合卡尔曼滤波器较好地克服了均值漂移算法不能跟踪快速目标的问题，计算卡尔曼残差来判断目标是否发生遮挡，从而选择卡尔曼滤波或是线性预测来估计目标位置。通过对跟踪过程中目标模板的偏移校正来消除跟踪误差的累积，保证目标空间直方图能准确描述目标，提高跟踪的鲁棒性。
(4)为了解决单一视觉信息在环境变化，以及目标平移、旋转和尺度变化等情况下描述目标不够充分、目标跟踪不够稳定的问题，提出了一种基于自适应混合似然模型的粒子滤波跟踪算法。在状态矢量中增加表示目标几何变换的变量，通过仿射变换来处理目标的运动变化。目标观测的描述融合了多种特征信息，似测模型能根据当前的跟踪环境的变换实时切换。由于算法始终利用对当前跟踪场景可分性好的特征信息跟踪目标，有效地提高了粒子滤波跟踪算法在复杂场景下的稳健性。
论文对复杂场景下运动目标检测与跟踪算法进行了比较深入的研究，重点针对目标分割、目标建模、遮挡、鲁棒跟踪等关键技术问题，提出了新的、有效的解决思路和实现方案。
With the development and popularization of computer science, electronic technology,automatic control and artificial intelligence, the intelligent visual surveillance plays veryimportant roles in military, industry, human-computer interaction, intelligent transformand science researching, etc, and it is of a wide development prospect as well. So peoplepay more and more attention to the researches of sequential images motion detection andvisual tracking, which is a key technology of the intelligent visual surveillance and is anactive research topic in the image processing and computer vision. At present, movingtarget detection and tracking is not well-considered, many problems and difficult points intheory research and in applications are still unsolved. However, many issues such as weakdistinguishing image features, background clutter, occlusion, in addition, the targetmovement often behaves very complicated in real environment (e.g. target size, shapechanging, move speed and path, illumination changing, the similarity of target color andbackground color, and background stability, etc), can affect the effective observation of thetracked targets in images and make robust tracking algorithms designing a very difficultproblem. Therefore, the researching on moving target detection and tracking undercomplicated background has both important theory significance and application value.Aiming at resolving the difficult problems of the robust visual tracking, we studied severalkey technologies under complicated environment from movement target detection andsegmentation, target area feature acquiring, target describing, and robust tracking. Themain contents and contributions of this dissertation are summarized as follows:
(1) The moving target detection and segmentation have been studied in stationaryscene. An effective adaptive background updating method based on Gaussian mixturemodel (GMM) was presented. The number of mixture components of GMM is estimatedaccording to the frequency of pixel value changes, the performance of GMM can beeffectively improved with the modified background learning and update, new distributiongeneration rule and morphological reconstruction based on spatial and temporal pixelsinformation. The detection of illumination great change and shadow removal were alsoproposed.
(2) To enhance the performances of object tracking in stationary scene, a trackingmethod based on adaptive color segmentation and object part model was presented. In thiswork, the foreground blobs are obtained by background subtract. Object parts in the part model are generated online by the color segmentation based on mean shift andregion-growth. The constraints between parts and region features are taken into accountand used to perform objects tracking. The algorithm can solve the partial occlusion andobject deformation problem well.
(3) To improve theoretic limitation of the traditional Mean shift, a novel targettracking algorithm was presented. Firstly, a new color space is partitioned into subspacesby considering the weighted number of pixels with feature vectors cluster, and describingthe pixel coordinates with Gaussian distribution. Then the target model and the candidateare constructed through an improved spatial histogram, which has ability to surmount thesimilarity between target and background. Finally, the affine transform is establishedcombining comer and edge detector to update tracking window. The algorithm is able tohandle the fast target tracking and occlusion by combining kalman filter and Mean shift.Besides, the algorithm has better effect and robust through eliminating the tracking errorusing the drift correction of target template.
(4) To resolve the problem of single visual cue in different enviroments and posechanging, a particle filter tracking algorithm based on cues integration mechanism andadaptive observation likelihood is proposed. The multiple cues are adopted to representtarget, the likelihood model is constructed on-line with the reliable cues. Besides, hiddenvariables indicating geometric transformation are also augmented in the staterepresentation, and affine transformation is used to handle the movement changing oftarget.
In the dissertation, the target detection and tracking algorithms have been studiedextensively under complicated environments, robust and practical methods were proposedto solve the key technologies, such as target segmentation, target modeling, occlulsion,and robust target tracking.
(1)对静态背景下运动目标的检测与分割进行了研究。针对复杂背景、光照变化、阴影等影响目标检测的问题，提出了一种有效的自适应混合高斯背景建模算法，各像素点根据其像素值出现的混乱程度采取不同个数的高斯分布描述，重新设计了背景模型的学习与更新方法以及高斯分布生成准则；采用基于联合像素时空信息的分割与形态学重构方法使得前景目标分割的性能得到了有效地提高。同时文中也给出了阴影检测与抑制、光照突变的处理方法。该算法能够快速准确地建立背景模型，准确分割前景目标。
(2)针对固定监控场景提出了一种基于色彩分割与目标局部模型匹配的非刚性目标跟踪算法。利用自适应混合高斯背景模型提取前景运动目标，通过基于均值漂移滤波与区域生长的色彩分割算法建立目标局部模型并实时更新，结合区域约束条件和目标模型区域匹配实现目标的跟踪。算法能有效地解决跟踪过程中目标局部遮挡与形变问题。
(3)针对基于核的跟踪算法中颜色直方图对目标特征描述较弱、跟踪过程中核函数带宽保持不变、无法实现模板更新的缺点，提出了一种基于偏移校正的核空间直方图目标跟踪算法。在特定的色彩空间中，联合像素颜色与空间信息建立目标模型，即建立目标颜色直方图的同时用高斯分布对直方图各区间内的像素坐标建模，有效地解决了目标与背景颜色相似情况下的跟踪问题。通过目标特征点匹配估算目标仿射模型参数实现跟踪窗的更新。结合卡尔曼滤波器较好地克服了均值漂移算法不能跟踪快速目标的问题，计算卡尔曼残差来判断目标是否发生遮挡，从而选择卡尔曼滤波或是线性预测来估计目标位置。通过对跟踪过程中目标模板的偏移校正来消除跟踪误差的累积，保证目标空间直方图能准确描述目标，提高跟踪的鲁棒性。
(4)为了解决单一视觉信息在环境变化，以及目标平移、旋转和尺度变化等情况下描述目标不够充分、目标跟踪不够稳定的问题，提出了一种基于自适应混合似然模型的粒子滤波跟踪算法。在状态矢量中增加表示目标几何变换的变量，通过仿射变换来处理目标的运动变化。目标观测的描述融合了多种特征信息，似测模型能根据当前的跟踪环境的变换实时切换。由于算法始终利用对当前跟踪场景可分性好的特征信息跟踪目标，有效地提高了粒子滤波跟踪算法在复杂场景下的稳健性。
论文对复杂场景下运动目标检测与跟踪算法进行了比较深入的研究，重点针对目标分割、目标建模、遮挡、鲁棒跟踪等关键技术问题，提出了新的、有效的解决思路和实现方案。
With the development and popularization of computer science, electronic technology,automatic control and artificial intelligence, the intelligent visual surveillance plays veryimportant roles in military, industry, human-computer interaction, intelligent transformand science researching, etc, and it is of a wide development prospect as well. So peoplepay more and more attention to the researches of sequential images motion detection andvisual tracking, which is a key technology of the intelligent visual surveillance and is anactive research topic in the image processing and computer vision. At present, movingtarget detection and tracking is not well-considered, many problems and difficult points intheory research and in applications are still unsolved. However, many issues such as weakdistinguishing image features, background clutter, occlusion, in addition, the targetmovement often behaves very complicated in real environment (e.g. target size, shapechanging, move speed and path, illumination changing, the similarity of target color andbackground color, and background stability, etc), can affect the effective observation of thetracked targets in images and make robust tracking algorithms designing a very difficultproblem. Therefore, the researching on moving target detection and tracking undercomplicated background has both important theory significance and application value.Aiming at resolving the difficult problems of the robust visual tracking, we studied severalkey technologies under complicated environment from movement target detection andsegmentation, target area feature acquiring, target describing, and robust tracking. Themain contents and contributions of this dissertation are summarized as follows:
(1) The moving target detection and segmentation have been studied in stationaryscene. An effective adaptive background updating method based on Gaussian mixturemodel (GMM) was presented. The number of mixture components of GMM is estimatedaccording to the frequency of pixel value changes, the performance of GMM can beeffectively improved with the modified background learning and update, new distributiongeneration rule and morphological reconstruction based on spatial and temporal pixelsinformation. The detection of illumination great change and shadow removal were alsoproposed.
(2) To enhance the performances of object tracking in stationary scene, a trackingmethod based on adaptive color segmentation and object part model was presented. In thiswork, the foreground blobs are obtained by background subtract. Object parts in the part model are generated online by the color segmentation based on mean shift andregion-growth. The constraints between parts and region features are taken into accountand used to perform objects tracking. The algorithm can solve the partial occlusion andobject deformation problem well.
(3) To improve theoretic limitation of the traditional Mean shift, a novel targettracking algorithm was presented. Firstly, a new color space is partitioned into subspacesby considering the weighted number of pixels with feature vectors cluster, and describingthe pixel coordinates with Gaussian distribution. Then the target model and the candidateare constructed through an improved spatial histogram, which has ability to surmount thesimilarity between target and background. Finally, the affine transform is establishedcombining comer and edge detector to update tracking window. The algorithm is able tohandle the fast target tracking and occlusion by combining kalman filter and Mean shift.Besides, the algorithm has better effect and robust through eliminating the tracking errorusing the drift correction of target template.
(4) To resolve the problem of single visual cue in different enviroments and posechanging, a particle filter tracking algorithm based on cues integration mechanism andadaptive observation likelihood is proposed. The multiple cues are adopted to representtarget, the likelihood model is constructed on-line with the reliable cues. Besides, hiddenvariables indicating geometric transformation are also augmented in the staterepresentation, and affine transformation is used to handle the movement changing oftarget.
In the dissertation, the target detection and tracking algorithms have been studiedextensively under complicated environments, robust and practical methods were proposedto solve the key technologies, such as target segmentation, target modeling, occlulsion,and robust target tracking.
引文
[1] 王亮，胡卫明，谭铁牛．人运动的视觉分析综述．计算机学报，2002：25(3)：225-237
[2] Collins R, Lipton A J, Kanade T, et al. A system for video surveillance and monitoring: VSAM final report. Technical Report: CMU-RI-TR-00-12, Carnegie Melon University, Pittsburgh, Peen, America, 2000
[3] Haritaoglu I, Harwood D, Davis L S. W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809-830
[4] 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
[5] Pavlidis I, Morellas V, Tsiamyrtzis P, et al. Urban surveillance system: From the laboratory to the commercial world. Proceedings of the IEEE, 2001, 89(10): 1478-1497
[6] Bogaert M, Chleq N, Cornez P, et al. The PASSWORDS project. Proceedings of IEEE International Conference on Image Processing. Switzerland: Lausanne, 1996, 3: 675-678
[7] Matsuyama T. Cooperative distributed vision. Proceedings of DARPA Image Understanding Workshop, 1998. 365-384
[8] Ferryman J M, Maybank S J, Worrall A D. Visual surveillance for moving vehicle. International Journal of Computer Vision, 2000, 37(2): 187-197
[9] Zhu Z, Xu G, Yang B, et al. VISATRAM: A real-time vision system for automatic traffic monitoring. Image and Vision Computing, 2000, 18(10): 781-794
[10] Coifman B, Beymer D, Mclauchlan P, et al. A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C, 1998, 6(4): 271-288
[11] Tai J, Tseng S, Lin C, et al. Real-time image tracking for automatic traffic monitoring and enforcement application. Image and Vision Computing, 2004, 22(6): 485-501
[12] 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): 1267-1278
[13] Pai C, Tyan H, Liang Y, et al. Pedestrian detection and tracking at crossroads.Pattern Recognition. 2004,37(5): 1025-1034
[14] Tsao T, Wen Z. Image-based target tracking through rapid orientation change.Optical Engineering, 2002,41(3): 697-703
[15] Tao H, Sawhney H S, Kumar R. Object tracking with Bayesian estimation of dynamic layer representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(1): 75-89
[16] Choi H I, Rhee P K. Head gesture recognition using HMMs. Expert system with Applications, 1999,17(3): 213-221
[17] Viola P, Jones M. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): 137-154
[18] Tieu K, Viola P. Boosting image retrieval. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA, 2000, 1: 228-235
[19] Strieker D, Kettenbach T. Real-time and markerless vision-based tracking for outdoor augmented reality application. In: Proc. IEEE and ACM International Symposium on Augmented Reality, New York, NY, USA 2001. 189-190
[20] Desouza G N, Kak A C. Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(2): 237-267
[21] Lipton A J, Fujiyoshi H, Patil R S. Moving target classification and tracking from real-time video. In: Proc. IEEE Workshop on Applications of Computer Vision,Princeton, NJ, 1998.8-14
[22] Neri A, Colonnese S, Russo G, et al. Automatic moving object and background separation. Signal Processing, 1998, 66(2): 219-232
[23] Moscheni F, Bhattacharjee S, Kunt M. Spatialtemporal segmentation based on region merging. IEEE Transaction on Pattern Analysis and Machine Intelligence,1998, 20(9): 897-915
[24] Barron J, Fleet D, Beauchemin S. Performance of optical flow techniques.International Journal of Computer Vision, 1994, 12(1): 42-77
[25] Lucas B D and 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. 674-679
[26] Horn B, Schunch B. Detemining optical flow. Artificial Intelligence. 1981, 17:
??185-203
[27] Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition,Fort Collins, CO, USA, 1999, 2: 246-252
[28] Ohta N. A statistical approach to background subtraction for surveillance systems.International Conference on Computer Vision, 2001, 2: 481-486
[29] Mittal A, Paragios N. Motion-based background subtraction using adaptive kernel density estimation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2004, 2: 302-309
[30] Horprasert T, Harwood D, Davis L. A Statistical approach for real-time robust background subtraction and shadow detection. In: Proc. International Conference on Computer Vision, 1999. 1-19
[31] McKenna S, Jabri S, Duric Z, et al. Tracking groups of people. Computer Vision and Image Understanding, 2000, 80(1): 42-56
[32] Sun H, Feng T, Tan T. Robust extraction of moving objects from image sequences.In: Proc. the 4th Asian Conference on Computer Vision, Taiwan, 2000. 961-964
[33] Cucchiara R, Piccardi M, Prati A. Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10): 1337-1342
[34] Gloyer B, Aghajan H, Siu K Y, et al. Video-based freeway monitoring system using recursive vehicle tracking. In: Proc. SPIE on Image and video processing, 1995,2421:173-180
[35] Toyama K, Krumm J, Brumitt B, et al. Wallflower: Principles and practice of background maintenance. In: Proc. International Conference on Computer Vision,1999.255-261
[36] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. In: Proc. International Conference on Computer Vision, 1999. 751-767
[37] 侯志强,韩嵩昭.视觉跟踪技术综述,自动化学报,2006, 32(4): 603-617
[38] Collins R, Lipton A, et al. Algorithms for cooperative multisensor surveillance.Proceedings of the IEEE, 2001, 89(10): 1456-1477
[39] Magee D. Tracking multiple vehicles using foreground, background and motion models. Image and Vision Computing, 2004, 22(21): 143-155
[40] Koller D, Daniilidis K, Nagel H. Model-based object tracking in monocular image sequences of road traffic scene. International Journal of Computer Vision, 1993, 10(31): 257-281
[41] Dubuisson Jolly M, Lakshmanan S, Jain A K. Vehicle segmentation and classification using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(3): 293-308
[42] Bar-Shalom Y, Fortmann T. Tracking and Data Association, Academic Press, 1988
[43] Dockstader S L, Tekalp A M. Multiple cameras tracking of interacting and occluded human motion. Proceedings of the IEEE, 2001, 89(10): 1441-1455
[44] Betke M, Haritaoglu E, Davis L S. Real-time multiple vehicle detection and tracking from a moving vehicle. Machine Vision and Applications, 2000, 12(2):69-83
[45] Zhao T, Nevatia R. Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1208-1221
[46] Yu T, Wu Y. Collaborative tracking of multiple targets. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA: IEEE Press,2004,1:834-841
[47] Burt P J, Yen C, Xu X. Local correlation measures for motion analysis: A comparative study. In: Proc. IEEE Conference on Pattern Recognition and Image Processing, 1982: 269-274
[48] Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003,25(5): 564-577
[49] Nummiaro K, Koller-Meier E, Svoboda T, et al. Color-based object tracking in multi-camera environments. In: Proc. the 25th Pattern Recognition Symposium,Springer LNCS 2781, 2003: 591-599
[50] Koller D, Weber J, Huang T, et al. Towards robust automatic traffic scene analysis in real-time. In: Proc. International Conference on Pattern Recognition, 1994: 126-131
[51] Canny J F. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 6(8): 679-698
[52] Smith S, Brady J. SUSUN: A new approach to low level image processing.International Journal of Computer Vision, 1997, 23(1): 45-78
[53] Jain A, Zhong K, Lakshmanan Y. Object matching using deformable templates.IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(3):267-278
[54] Yu Z, Jain A K, Dubuisson-Jolly M. Object tracking using deformable templates.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(5):
锘??544-549
[55] Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. International Journal of Computer Vision, 1988, 1(4): 321-331
[56] Terzopoulos D, Szeliski R. Tracking with Kalman snakes. Active Vision, Blake A and Yuille A, Eds. Cambridge, MA: MIT Press, 1992: 3-20
[57] Peterfreund N. Robust tracking of position and velocity with Kalman snakes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(6): 564-569
[58] Paragios N, Deriche R. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(3): 266-280
[59] Gennery D. Tracking known three-dimensional objects. In: Proc. International Conference on Artifical Intelligence. 1982: 13-17
[60] Watcher S, Nagel H H. Tracking persons in monocular image sequences. Computer Vision and Image Understanding, 1999, 74(3): 174-192
[61] Kakadiaris L, Metaxas D. Model-based estimation of 3D human motion. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000,22(12): 1453-1459
[62] Hu W M, Tan T N, Wang L, et al. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics: Applications and Reviews, 2004, 34(3): 334-352
[63] 周宏人,敬忠良,王培德.机动目标跟踪.北京:购房工业出版社 1994:10
[64] Blom H A, Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coefficient. IEEE Transactions on Automatic Control, 1988,33(8): 780-783
[65] Strieker M., Orengo M. Similarity of color images, In: Proc. SPIE Storage and Retrieval for Image and Video Databases, 1995, 2420: 381-392
[66] Birchfield S T and Rangarajan S. Spatiograms versus histograms for region-based tracking. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition,San Diego, CA, United States, 2005, 2: 1158-1163
[67] Perez P, Hue C, Vermaak J, et al. Color-based probabilistic tracking. In: Proc. 7th European Conference on Computer Vision, Springer LNCS 2350, 2002: 661-675
[68] Nummiaro K, Koller-Meier E, Van Gool L. An adaptive color-based particle filter.Image and Vision Computing, 2003, 21(1): 99-110
[69] Haralick R M, Shapiro L G Statistical and structural approaches to texture.Proceedings of IEEE, 1979, 67(5): 786-804
[70] Tamura H, Mori S. Texture features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybemetics, 1978, 8(6): 460-473
[71] Brasnett P, Mihaylova L, Canagarajah N, et aI. Particle filtering with multiple cues for object tracking in video sequences. In: Proc. SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology, San Jose Califomia, USA, 2005, 5685: 430-440
[72] Manjunath B S, Ma M Y. Texture features for browsing and retrieval of image data. IEEE Transaction on Pattern Analysis and Machine Intelligence, 18(8): 837-842
[73] Bar-Shalom Y, Li X R, Kirubarajan T. Estimation with applications to tracking and navigation: Theory, Algorithms, and Software. New York, Wiley, 2001
[74] Julier S J, Uhlmann J K. A new method for the nonlinear transformation of means and covariance in filters and estimators. IEEE Transaction on Automatic Control, 2000, 45(3): 477-482
[75] Liu J S, Chen R. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 1998, 93(443): 1032-1044
[76] Hue C, Le Cadre J P, Perez P. Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Transaction on Signal Processing, 2002, 50 (2): 309-325
[77] Isard M, Blake A. Condensation-Conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29(1): 5-28
[78] Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE Proceedings on Radar and Signal Processing, 1993, 140(2): 107-113
[79] Arulampalam S, Maskell S, Gordon N, et al. A tutorial on particle filters for online non-linear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188
[80] Doucet A., Godsill S. On sequential Monte Carlo sampling methods for Baysian filtering. Statistics and Computing, 2000, 10: 197-208
[81] Silverman B W. Density estimation for statistics and data analysis. Chapman and Hall, 1986
[82] 李乡儒，吴福朝，胡占义．均值漂移算法的收敛性．软件学报，2005，16(3)：365-374
[83] 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 Conforence on Automatic Face and Gesture Recognition, 1998: 222-227
[84] Long W, Yang Y. Stationary background generation: An alternative to the difference of two images. Pattern Recognition, 1990, 23(12): 1351-1359
[85] Komprobst P, Deriche R, Aubert G Image sequence analysis via partial difference equations. Journal of Mathematical Imaging and Vision, 1999,11(1): 5-26
[86] Halevy G, Weinshall D. Motion of disturbances: detection and tracking of multi-body non-rigid motion. Machine Vision and Applications, 1999, 11(3):122-137
[87] 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): 1499-1504
[88] Monnet A, Mittal A, Paragios N, et al. Background modeling and subtraction of dynamic scenes. In: Proc. the 9th IEEE International Conference on Computer Vision, 2003: 1-8
[89] Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection. IEEE Transaction on Pattern Recognition and Machine Intelligence, 2005, 27(11):1778-1792
[90] Stauffer C, GrimsonW. Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757
[91] 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
[92] Lee D S, Hull J, ErolB. A Bayesian framework for Gaussian mixture background modeling. In: Proc. IEEE International Conference on Image Processing, 2003:973-976
[93] Zivkovic Z, F van der Heijden. Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26(5): 651-656
[94] Javed O, Shafique K, Shah M. A hierarchical approach to robust background subtraction using color and gradient information. In: Proc. IEEE Workshop Motion Video Computing, 2002: 22-27
[95] Tian Y L, Lu M, Hampapur A. Robust and efficient foreground analysis for real-time video surveillance. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2005: 1182-1187
[96] Dar-shyang Lee. Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(5): 827-832
[97] Cristani M, Bicego M, Murino V. Integrated region- and pixel-based approach to background modeling. In: Proc. IEEE Workshop on Motion and Video Computing,2002, 3: 3-8
[98] Wang H, Suter D. A Re-evaluation of mixture-of-Gaussian background modeling.IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005,2: 1017-1020
[99] 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
[100] Cheng J. Flexible background mixture models for foreground segmentation. Image and Vision Computing, 2006, 24: 473-482
[101] Stauder J, Mech R, Ostermann J. Detection of Moving cast shadows for object segmentation. IEEE Transactions on Multimedia, 1999,1(1): 65-76
[102] Leone A, Distante C, Buccolieri F. A texture-based approach for shadow detection.IEEE Conference on Advanced Video and Signal Based Surveillance, 2005:371-376
[103] Leone A, Distante C. Shadow detection for moving objects based on texture analysis. Pattern Recognition, 2007,40(4): 1222-1233
[104] Bai Sheng C, Yun Qi 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, 2004:137-142
[105] Martel-Brisson N, Zaccarin A. Moving cast shadow detection from a Gaussian mixture shadow model. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, 2005, 2: 643-648
[106] 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): 1133-1146
[107] Prati A, Mikic I, Trivedi M, et al. Detecting moving shadows: Formulation,algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 918-924
[108] Wang J M, Chung Y C, Chang C L, et al. Shadow detection and removal for traffic images. In: Proc. IEEE International Conference on Networking, Sensing&Control,Taipei, Taiwan, 2004: 649-654
[109] Cucchiara R. Improving shadow suppression in moving object detection with HSV color information. In: Proc. Intelligent Transportation Systems, Oakland (CA), USA,2001:334-339
[110] Polana R, Nelson R. Low level recognition of human motion. In: Proc. IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, 1994: 77-82
[111] Segen J, Pingali S. A camera-based system for tracking people in real time. In: Proc.International Conference on Pattern Recognition, 1996: 63-67
[112] Jang D S, Choi H I. Active models for tracking moving objects. Pattern Recognition,2000,33(7): 1135-1146
[113] Cucchiara R, Andrea P, Vezzani R. Real-time motion segmentation from moving cameras. Real-Time Imaging, 2004,10: 127-143
[114] Park S, Aggarwal J K. Simultaneous tracking of multiple body parts of interacting persons. Computer Vision and Image Understanding, 2005,102(1): 1-21
[115] Fukunaga K, Hostetler LD. The estimation of the gradient of a density function with applications in pattern recognition. IEEE Transaction on Information Theory, 1975,21(1): 32-40
[116] Cheng Y. Mean shift, mode seeking and clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799
[117] Comaniciu D, Ramesh V. Mean shift and optimal prediction for efficient object tracking. In: IEEE International Conference on Image Processing, 2000, 3: 70-73
[118] Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition,Stoughton: Printing House, 2000, 2: 142-149
[119] Bradski G R. Computer vision face tracking for use in a perceptual user interface.Intel Technology Journal Q2, 1998: 1-15
[120] Collins R T. Mean-shift blob tracking through scale space. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2003, 2: 234-240
[121]彭宁嵩,杨杰.Mean-Shift跟踪算法中核函数窗宽的自动选.软件学报,2005,16(9): 1542-1550
[122] Nguyen H T, Wowing M, Boomgaard R V D. Occlusion robust adaptive template tracking. In: Proc. IEEE Conference on Computer Vision, Vancouver, BC, Canada, 2001:678-683
[123] Ning Song P, Jie Y, Zhi L. Mean shift blob tracking with kernel histogram filtering and hypothesis testing. Pattern Recognition Letters, 2005,26(5): 605-614
[124] Yang C, Duraiswami R, Davis L. Efficient mean shift tracking via a new similarity measure. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2005: 176-183
[125] Yang C, Duraiswami R, Gumerov N A, et al. Improved fast gauss transform and efficient kernel density estimation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, France, 2003:464-471
[126] Zheng Q, Chellappa R. A computational vision approach to image registration.IEEE Transaction on Image Processing, 1993, 2(3): 311-326
[127] Wu Y, Huang T S. Robust visual tracking by integrating multiple cues based on co-inference learning. International Journal of Computer Vision, 2004, 88(1): 55-71
[128] Isard M, Blake A. ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. In: Proc. 5th European Conference Computer Vision,1998,1:893-908
[129] Birchfield S. Elliptical head tracking using intensity gradients and color histograms.In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, USA, 1998: 232-237
[130] Taylor G, Kleeman L. Fusion of multimodal visual cues for model-based object tracking. In: Australasian Conference on Robotics and Automation, Brisbane,Australia, 2003
[131] Li P H, Chaumette F, Perales F J, et al. Image cues fusion for object tracking based on particle filter. Springer LNCS 3179,2004: 99-110
[132] Kumar P, Brooks M J, Dick A R. Adaptive multiple object tracking using color and segmentation cues. Asian Conference on Computer Vision, Springer LNCS 4843,2007: 853-863
[133] Perez P, Vermaak J, Blake A. Data fusion for visual tracking with particles.Proceedings of the IEEE, 2004, 92(3): 495-513
[134] Xu X, Li B. Head tracking using particle filter with intensity gradient and color histogram. In: Proc. IEEE Conference on Multimedia and Expo, 2005: 888-891
[135] Triesch J, Malsburg C. Democratic integration: self-organized integration of adaptive cues. Neural Computation, 2001, 13(9): 2049-2074
[136] Spengler M, Schiele B. Towards robust multi-cue integration for visual tracking. Machine Vision and Applications, 2003, 14(1): 50-58
[137] Shen C, Hengel A, Dick A. Probabilistic multiple cue integration for particle filter based tracking. In: Proc. International Conference on Digital Image Computing,Sydney, 2003,1:399-408
[138] Kotecha J H, Djuric' P M. Gaussian particle filtering. IEEE Transactions on Signal Processing, 2003, 51(10): 2592-2601
[139] Kotecha J H, Djuric' P M. Gaussian sum particle filtering. IEEE Transactions on Signal Processing, 2003, 51: 2602-2612
[140] Ellis T. Multi-Camera Video Surveillance. In: Proc. 36th Annual International Carnahan Conference on Security Technology, 2002: 228-233
[141] Bramberger M, Quaritsch M, Winkler T, et al. Integrating Multi-Camera Tracking into a Dynamic Task Allocation System for Smart Cameras. IEEE Conference on Advanced Video and Signal Based Surveillance, 2005: 474-479
[142] Black J, Ellis T, Rosin P. Multi View Image Surveillance and Tracking. In: Proc. the Workshop on Motion and Video Computing, 2002: 169-174
[143] Niu W, Long J, Han D, et al. Human Activity Detection and Recognition for Video Surveillance. IEEE International Conference on Multimedia and Expo, 2004, 1:719-722
[144] Duong T V, Phung D Q, Bui H H, et al. Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model.International Conference on Pattern Recognition, Hong Kong, 2006, 3: 202-207
[2] Collins R, Lipton A J, Kanade T, et al. A system for video surveillance and monitoring: VSAM final report. Technical Report: CMU-RI-TR-00-12, Carnegie Melon University, Pittsburgh, Peen, America, 2000
[3] Haritaoglu I, Harwood D, Davis L S. W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809-830
[4] 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
[5] Pavlidis I, Morellas V, Tsiamyrtzis P, et al. Urban surveillance system: From the laboratory to the commercial world. Proceedings of the IEEE, 2001, 89(10): 1478-1497
[6] Bogaert M, Chleq N, Cornez P, et al. The PASSWORDS project. Proceedings of IEEE International Conference on Image Processing. Switzerland: Lausanne, 1996, 3: 675-678
[7] Matsuyama T. Cooperative distributed vision. Proceedings of DARPA Image Understanding Workshop, 1998. 365-384
[8] Ferryman J M, Maybank S J, Worrall A D. Visual surveillance for moving vehicle. International Journal of Computer Vision, 2000, 37(2): 187-197
[9] Zhu Z, Xu G, Yang B, et al. VISATRAM: A real-time vision system for automatic traffic monitoring. Image and Vision Computing, 2000, 18(10): 781-794
[10] Coifman B, Beymer D, Mclauchlan P, et al. A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C, 1998, 6(4): 271-288
[11] Tai J, Tseng S, Lin C, et al. Real-time image tracking for automatic traffic monitoring and enforcement application. Image and Vision Computing, 2004, 22(6): 485-501
[12] 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): 1267-1278
[13] Pai C, Tyan H, Liang Y, et al. Pedestrian detection and tracking at crossroads.Pattern Recognition. 2004,37(5): 1025-1034
[14] Tsao T, Wen Z. Image-based target tracking through rapid orientation change.Optical Engineering, 2002,41(3): 697-703
[15] Tao H, Sawhney H S, Kumar R. Object tracking with Bayesian estimation of dynamic layer representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(1): 75-89
[16] Choi H I, Rhee P K. Head gesture recognition using HMMs. Expert system with Applications, 1999,17(3): 213-221
[17] Viola P, Jones M. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): 137-154
[18] Tieu K, Viola P. Boosting image retrieval. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA, 2000, 1: 228-235
[19] Strieker D, Kettenbach T. Real-time and markerless vision-based tracking for outdoor augmented reality application. In: Proc. IEEE and ACM International Symposium on Augmented Reality, New York, NY, USA 2001. 189-190
[20] Desouza G N, Kak A C. Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(2): 237-267
[21] Lipton A J, Fujiyoshi H, Patil R S. Moving target classification and tracking from real-time video. In: Proc. IEEE Workshop on Applications of Computer Vision,Princeton, NJ, 1998.8-14
[22] Neri A, Colonnese S, Russo G, et al. Automatic moving object and background separation. Signal Processing, 1998, 66(2): 219-232
[23] Moscheni F, Bhattacharjee S, Kunt M. Spatialtemporal segmentation based on region merging. IEEE Transaction on Pattern Analysis and Machine Intelligence,1998, 20(9): 897-915
[24] Barron J, Fleet D, Beauchemin S. Performance of optical flow techniques.International Journal of Computer Vision, 1994, 12(1): 42-77
[25] Lucas B D and 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. 674-679
[26] Horn B, Schunch B. Detemining optical flow. Artificial Intelligence. 1981, 17:
??185-203
[27] Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition,Fort Collins, CO, USA, 1999, 2: 246-252
[28] Ohta N. A statistical approach to background subtraction for surveillance systems.International Conference on Computer Vision, 2001, 2: 481-486
[29] Mittal A, Paragios N. Motion-based background subtraction using adaptive kernel density estimation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2004, 2: 302-309
[30] Horprasert T, Harwood D, Davis L. A Statistical approach for real-time robust background subtraction and shadow detection. In: Proc. International Conference on Computer Vision, 1999. 1-19
[31] McKenna S, Jabri S, Duric Z, et al. Tracking groups of people. Computer Vision and Image Understanding, 2000, 80(1): 42-56
[32] Sun H, Feng T, Tan T. Robust extraction of moving objects from image sequences.In: Proc. the 4th Asian Conference on Computer Vision, Taiwan, 2000. 961-964
[33] Cucchiara R, Piccardi M, Prati A. Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10): 1337-1342
[34] Gloyer B, Aghajan H, Siu K Y, et al. Video-based freeway monitoring system using recursive vehicle tracking. In: Proc. SPIE on Image and video processing, 1995,2421:173-180
[35] Toyama K, Krumm J, Brumitt B, et al. Wallflower: Principles and practice of background maintenance. In: Proc. International Conference on Computer Vision,1999.255-261
[36] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. In: Proc. International Conference on Computer Vision, 1999. 751-767
[37] 侯志强,韩嵩昭.视觉跟踪技术综述,自动化学报,2006, 32(4): 603-617
[38] Collins R, Lipton A, et al. Algorithms for cooperative multisensor surveillance.Proceedings of the IEEE, 2001, 89(10): 1456-1477
[39] Magee D. Tracking multiple vehicles using foreground, background and motion models. Image and Vision Computing, 2004, 22(21): 143-155
[40] Koller D, Daniilidis K, Nagel H. Model-based object tracking in monocular image sequences of road traffic scene. International Journal of Computer Vision, 1993, 10(31): 257-281
[41] Dubuisson Jolly M, Lakshmanan S, Jain A K. Vehicle segmentation and classification using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(3): 293-308
[42] Bar-Shalom Y, Fortmann T. Tracking and Data Association, Academic Press, 1988
[43] Dockstader S L, Tekalp A M. Multiple cameras tracking of interacting and occluded human motion. Proceedings of the IEEE, 2001, 89(10): 1441-1455
[44] Betke M, Haritaoglu E, Davis L S. Real-time multiple vehicle detection and tracking from a moving vehicle. Machine Vision and Applications, 2000, 12(2):69-83
[45] Zhao T, Nevatia R. Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1208-1221
[46] Yu T, Wu Y. Collaborative tracking of multiple targets. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA: IEEE Press,2004,1:834-841
[47] Burt P J, Yen C, Xu X. Local correlation measures for motion analysis: A comparative study. In: Proc. IEEE Conference on Pattern Recognition and Image Processing, 1982: 269-274
[48] Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003,25(5): 564-577
[49] Nummiaro K, Koller-Meier E, Svoboda T, et al. Color-based object tracking in multi-camera environments. In: Proc. the 25th Pattern Recognition Symposium,Springer LNCS 2781, 2003: 591-599
[50] Koller D, Weber J, Huang T, et al. Towards robust automatic traffic scene analysis in real-time. In: Proc. International Conference on Pattern Recognition, 1994: 126-131
[51] Canny J F. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 6(8): 679-698
[52] Smith S, Brady J. SUSUN: A new approach to low level image processing.International Journal of Computer Vision, 1997, 23(1): 45-78
[53] Jain A, Zhong K, Lakshmanan Y. Object matching using deformable templates.IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(3):267-278
[54] Yu Z, Jain A K, Dubuisson-Jolly M. Object tracking using deformable templates.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(5):
锘??544-549
[55] Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. International Journal of Computer Vision, 1988, 1(4): 321-331
[56] Terzopoulos D, Szeliski R. Tracking with Kalman snakes. Active Vision, Blake A and Yuille A, Eds. Cambridge, MA: MIT Press, 1992: 3-20
[57] Peterfreund N. Robust tracking of position and velocity with Kalman snakes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(6): 564-569
[58] Paragios N, Deriche R. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(3): 266-280
[59] Gennery D. Tracking known three-dimensional objects. In: Proc. International Conference on Artifical Intelligence. 1982: 13-17
[60] Watcher S, Nagel H H. Tracking persons in monocular image sequences. Computer Vision and Image Understanding, 1999, 74(3): 174-192
[61] Kakadiaris L, Metaxas D. Model-based estimation of 3D human motion. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000,22(12): 1453-1459
[62] Hu W M, Tan T N, Wang L, et al. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics: Applications and Reviews, 2004, 34(3): 334-352
[63] 周宏人,敬忠良,王培德.机动目标跟踪.北京:购房工业出版社 1994:10
[64] Blom H A, Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coefficient. IEEE Transactions on Automatic Control, 1988,33(8): 780-783
[65] Strieker M., Orengo M. Similarity of color images, In: Proc. SPIE Storage and Retrieval for Image and Video Databases, 1995, 2420: 381-392
[66] Birchfield S T and Rangarajan S. Spatiograms versus histograms for region-based tracking. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition,San Diego, CA, United States, 2005, 2: 1158-1163
[67] Perez P, Hue C, Vermaak J, et al. Color-based probabilistic tracking. In: Proc. 7th European Conference on Computer Vision, Springer LNCS 2350, 2002: 661-675
[68] Nummiaro K, Koller-Meier E, Van Gool L. An adaptive color-based particle filter.Image and Vision Computing, 2003, 21(1): 99-110
[69] Haralick R M, Shapiro L G Statistical and structural approaches to texture.Proceedings of IEEE, 1979, 67(5): 786-804
[70] Tamura H, Mori S. Texture features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybemetics, 1978, 8(6): 460-473
[71] Brasnett P, Mihaylova L, Canagarajah N, et aI. Particle filtering with multiple cues for object tracking in video sequences. In: Proc. SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology, San Jose Califomia, USA, 2005, 5685: 430-440
[72] Manjunath B S, Ma M Y. Texture features for browsing and retrieval of image data. IEEE Transaction on Pattern Analysis and Machine Intelligence, 18(8): 837-842
[73] Bar-Shalom Y, Li X R, Kirubarajan T. Estimation with applications to tracking and navigation: Theory, Algorithms, and Software. New York, Wiley, 2001
[74] Julier S J, Uhlmann J K. A new method for the nonlinear transformation of means and covariance in filters and estimators. IEEE Transaction on Automatic Control, 2000, 45(3): 477-482
[75] Liu J S, Chen R. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 1998, 93(443): 1032-1044
[76] Hue C, Le Cadre J P, Perez P. Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Transaction on Signal Processing, 2002, 50 (2): 309-325
[77] Isard M, Blake A. Condensation-Conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29(1): 5-28
[78] Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE Proceedings on Radar and Signal Processing, 1993, 140(2): 107-113
[79] Arulampalam S, Maskell S, Gordon N, et al. A tutorial on particle filters for online non-linear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188
[80] Doucet A., Godsill S. On sequential Monte Carlo sampling methods for Baysian filtering. Statistics and Computing, 2000, 10: 197-208
[81] Silverman B W. Density estimation for statistics and data analysis. Chapman and Hall, 1986
[82] 李乡儒，吴福朝，胡占义．均值漂移算法的收敛性．软件学报，2005，16(3)：365-374
[83] 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 Conforence on Automatic Face and Gesture Recognition, 1998: 222-227
[84] Long W, Yang Y. Stationary background generation: An alternative to the difference of two images. Pattern Recognition, 1990, 23(12): 1351-1359
[85] Komprobst P, Deriche R, Aubert G Image sequence analysis via partial difference equations. Journal of Mathematical Imaging and Vision, 1999,11(1): 5-26
[86] Halevy G, Weinshall D. Motion of disturbances: detection and tracking of multi-body non-rigid motion. Machine Vision and Applications, 1999, 11(3):122-137
[87] 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): 1499-1504
[88] Monnet A, Mittal A, Paragios N, et al. Background modeling and subtraction of dynamic scenes. In: Proc. the 9th IEEE International Conference on Computer Vision, 2003: 1-8
[89] Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection. IEEE Transaction on Pattern Recognition and Machine Intelligence, 2005, 27(11):1778-1792
[90] Stauffer C, GrimsonW. Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757
[91] 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
[92] Lee D S, Hull J, ErolB. A Bayesian framework for Gaussian mixture background modeling. In: Proc. IEEE International Conference on Image Processing, 2003:973-976
[93] Zivkovic Z, F van der Heijden. Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26(5): 651-656
[94] Javed O, Shafique K, Shah M. A hierarchical approach to robust background subtraction using color and gradient information. In: Proc. IEEE Workshop Motion Video Computing, 2002: 22-27
[95] Tian Y L, Lu M, Hampapur A. Robust and efficient foreground analysis for real-time video surveillance. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2005: 1182-1187
[96] Dar-shyang Lee. Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(5): 827-832
[97] Cristani M, Bicego M, Murino V. Integrated region- and pixel-based approach to background modeling. In: Proc. IEEE Workshop on Motion and Video Computing,2002, 3: 3-8
[98] Wang H, Suter D. A Re-evaluation of mixture-of-Gaussian background modeling.IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005,2: 1017-1020
[99] 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
[100] Cheng J. Flexible background mixture models for foreground segmentation. Image and Vision Computing, 2006, 24: 473-482
[101] Stauder J, Mech R, Ostermann J. Detection of Moving cast shadows for object segmentation. IEEE Transactions on Multimedia, 1999,1(1): 65-76
[102] Leone A, Distante C, Buccolieri F. A texture-based approach for shadow detection.IEEE Conference on Advanced Video and Signal Based Surveillance, 2005:371-376
[103] Leone A, Distante C. Shadow detection for moving objects based on texture analysis. Pattern Recognition, 2007,40(4): 1222-1233
[104] Bai Sheng C, Yun Qi 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, 2004:137-142
[105] Martel-Brisson N, Zaccarin A. Moving cast shadow detection from a Gaussian mixture shadow model. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, 2005, 2: 643-648
[106] 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): 1133-1146
[107] Prati A, Mikic I, Trivedi M, et al. Detecting moving shadows: Formulation,algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 918-924
[108] Wang J M, Chung Y C, Chang C L, et al. Shadow detection and removal for traffic images. In: Proc. IEEE International Conference on Networking, Sensing&Control,Taipei, Taiwan, 2004: 649-654
[109] Cucchiara R. Improving shadow suppression in moving object detection with HSV color information. In: Proc. Intelligent Transportation Systems, Oakland (CA), USA,2001:334-339
[110] Polana R, Nelson R. Low level recognition of human motion. In: Proc. IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, 1994: 77-82
[111] Segen J, Pingali S. A camera-based system for tracking people in real time. In: Proc.International Conference on Pattern Recognition, 1996: 63-67
[112] Jang D S, Choi H I. Active models for tracking moving objects. Pattern Recognition,2000,33(7): 1135-1146
[113] Cucchiara R, Andrea P, Vezzani R. Real-time motion segmentation from moving cameras. Real-Time Imaging, 2004,10: 127-143
[114] Park S, Aggarwal J K. Simultaneous tracking of multiple body parts of interacting persons. Computer Vision and Image Understanding, 2005,102(1): 1-21
[115] Fukunaga K, Hostetler LD. The estimation of the gradient of a density function with applications in pattern recognition. IEEE Transaction on Information Theory, 1975,21(1): 32-40
[116] Cheng Y. Mean shift, mode seeking and clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799
[117] Comaniciu D, Ramesh V. Mean shift and optimal prediction for efficient object tracking. In: IEEE International Conference on Image Processing, 2000, 3: 70-73
[118] Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition,Stoughton: Printing House, 2000, 2: 142-149
[119] Bradski G R. Computer vision face tracking for use in a perceptual user interface.Intel Technology Journal Q2, 1998: 1-15
[120] Collins R T. Mean-shift blob tracking through scale space. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2003, 2: 234-240
[121]彭宁嵩,杨杰.Mean-Shift跟踪算法中核函数窗宽的自动选.软件学报,2005,16(9): 1542-1550
[122] Nguyen H T, Wowing M, Boomgaard R V D. Occlusion robust adaptive template tracking. In: Proc. IEEE Conference on Computer Vision, Vancouver, BC, Canada, 2001:678-683
[123] Ning Song P, Jie Y, Zhi L. Mean shift blob tracking with kernel histogram filtering and hypothesis testing. Pattern Recognition Letters, 2005,26(5): 605-614
[124] Yang C, Duraiswami R, Davis L. Efficient mean shift tracking via a new similarity measure. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2005: 176-183
[125] Yang C, Duraiswami R, Gumerov N A, et al. Improved fast gauss transform and efficient kernel density estimation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, France, 2003:464-471
[126] Zheng Q, Chellappa R. A computational vision approach to image registration.IEEE Transaction on Image Processing, 1993, 2(3): 311-326
[127] Wu Y, Huang T S. Robust visual tracking by integrating multiple cues based on co-inference learning. International Journal of Computer Vision, 2004, 88(1): 55-71
[128] Isard M, Blake A. ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. In: Proc. 5th European Conference Computer Vision,1998,1:893-908
[129] Birchfield S. Elliptical head tracking using intensity gradients and color histograms.In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, USA, 1998: 232-237
[130] Taylor G, Kleeman L. Fusion of multimodal visual cues for model-based object tracking. In: Australasian Conference on Robotics and Automation, Brisbane,Australia, 2003
[131] Li P H, Chaumette F, Perales F J, et al. Image cues fusion for object tracking based on particle filter. Springer LNCS 3179,2004: 99-110
[132] Kumar P, Brooks M J, Dick A R. Adaptive multiple object tracking using color and segmentation cues. Asian Conference on Computer Vision, Springer LNCS 4843,2007: 853-863
[133] Perez P, Vermaak J, Blake A. Data fusion for visual tracking with particles.Proceedings of the IEEE, 2004, 92(3): 495-513
[134] Xu X, Li B. Head tracking using particle filter with intensity gradient and color histogram. In: Proc. IEEE Conference on Multimedia and Expo, 2005: 888-891
[135] Triesch J, Malsburg C. Democratic integration: self-organized integration of adaptive cues. Neural Computation, 2001, 13(9): 2049-2074
[136] Spengler M, Schiele B. Towards robust multi-cue integration for visual tracking. Machine Vision and Applications, 2003, 14(1): 50-58
[137] Shen C, Hengel A, Dick A. Probabilistic multiple cue integration for particle filter based tracking. In: Proc. International Conference on Digital Image Computing,Sydney, 2003,1:399-408
[138] Kotecha J H, Djuric' P M. Gaussian particle filtering. IEEE Transactions on Signal Processing, 2003, 51(10): 2592-2601
[139] Kotecha J H, Djuric' P M. Gaussian sum particle filtering. IEEE Transactions on Signal Processing, 2003, 51: 2602-2612
[140] Ellis T. Multi-Camera Video Surveillance. In: Proc. 36th Annual International Carnahan Conference on Security Technology, 2002: 228-233
[141] Bramberger M, Quaritsch M, Winkler T, et al. Integrating Multi-Camera Tracking into a Dynamic Task Allocation System for Smart Cameras. IEEE Conference on Advanced Video and Signal Based Surveillance, 2005: 474-479
[142] Black J, Ellis T, Rosin P. Multi View Image Surveillance and Tracking. In: Proc. the Workshop on Motion and Video Computing, 2002: 169-174
[143] Niu W, Long J, Han D, et al. Human Activity Detection and Recognition for Video Surveillance. IEEE International Conference on Multimedia and Expo, 2004, 1:719-722
[144] Duong T V, Phung D Q, Bui H H, et al. Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model.International Conference on Pattern Recognition, Hong Kong, 2006, 3: 202-207