基于视觉的运动目标跟踪算法及其在移动机器人中的应用
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摘要
运动目标跟踪一直是计算机视觉领域备受关注的前沿课题之一,在移动机器人定位与导航、多机器人编队、月球探测以及智能监控等方面都有非常重要的应用。经过几十年来学者们的不懈努力,运动目标检测与跟踪技术取得了长足的进步。但是,由于视觉跟踪系统应用环境复杂性(比如光照、遮挡等因素影响)以及目标本身的多样性,给目标检测、跟踪技术带来了极大的困难。
本文在研究传统视觉目标跟踪算法的基础上,对单目标跟踪、多目标跟踪、广义目标检测、立体视觉跟踪等问题,结合学术前沿知识,提出了新的算法,提高跟踪的准确性和鲁棒性。最后还应用到移动机器人目标识别、定位与跟踪上,具有较好的跟踪性能。
本文研究工作可以总结为以下几个方面：
(1)为了克服单个目标跟踪算法在复杂环境下跟踪精度不高的问题,提出了一种基于序贯检测机制的运动目标跟踪算法。该算法在序贯检测机制下,将粒子滤波、稀疏场主动轮廓和Camshift等算法结合。首先用基于颜色特征的粒子滤波估计最优跟踪窗口;而此跟踪窗口和目标的相似度决定是否采用稀疏场主动轮廓算法,同样目标轮廓和目标的相似度决定是否需要Camshift对轮廓进行修正。跟踪实验表明本章所提的算法在不同的复杂环境和目标有尺度、旋转、视角等姿态变化的情况下,具有较好的跟踪精度和鲁棒性。
(2)针对单一视觉特征的目标模型很难对环境中所有变化都具有足够的鲁棒性,提出了一种自适应多特征融合的核函数目标跟踪算法。该算法目标模型的概率分布用SIFT、颜色和运动特征的核函数线性加权来表示。目标的表征特征和运动特征相结合,可以提高跟踪的稳定性和精确性。跟踪窗口尺寸根据匹配到的SIFT特征对的仿射变化参数实时调整。每个特征核函数的权重跟随特征的显著性变化而自适应变化,从而可以最大限度地发挥每个特征的作用。实验表明该算法可以在不同的场景下跟踪目标,而且可以应对目标有姿态、尺度、旋转、视点变化和环境光照变化等情况,而且本算法的跟踪性能优于Camshift算法、基于SIFT特征目标跟踪、基于彩色SIFT目标跟踪算法。
(3)针对多目标跟踪问题,提出了一种基于信息分享机制的粒子滤波多目标跟踪算法,提高多目标跟踪效率。该算法将粒子群优化算法和蚁群优化算法的优化思想共同作用到粒子更新中,实现粒子之间信息共享,从而增强粒子的多样性和最优估计能力；同时分析了该算法的收敛性。实验表明,本算法能用较少的粒子达到较高的跟踪精度,而且跟踪性能优于传统粒子滤波和基于粒子群优化的粒子滤波算法。
(4)研究了广义目标检测算法,针对广义目标的复杂性和多样性,提出了一种嵌入Bag-of-words的Boosting目标检测算法。Boosting算法具有较好的检测效率,但是对于广义目标的复杂性和多样性,会存在一定的误检测,本文算法嵌入Bag-of-words算法,用其基于局部块特征,而且简单、鲁棒性好,对遮挡、复杂目标具有较好的分类性能等特性,对Boosting检测结果进行修正,剔除其误检测部分,从而提高广义目标检测精度。
(5)针对双目视觉比单目视觉具有更丰富的信息量,研究了基于双目视觉的目标跟踪算法,提出了两种基于双目视觉的移动机器人实时动态目标识别与定位的算法。一种算法首先采用SIFT算法提取目标特征,并结合双目视差特征进行目标匹配；然后通过区域增长算法进行目标区域的提取；最后结合双目视觉标定模型对目标进行定位。另外一种算法把基于序贯检测机制算法、双目视觉视差信息、标定模型相结合来完成双目视觉跟踪,再引入视差置信区间判据可有效减少噪声影响,提高运动目标定位精度。实验表明这两种算法在摄像机运动-目标运动情况下,能对动态目标进行有效地识别与定位。
Dynamic target tracking is always given to attention for computer vision, and it is widely applied in robot location and navigation, multi-robot formation, lunar exploration and intelligent surveillance. Significant progress has been made in targets tracking during the last few years. However, the task of robust tracking is challenging due to the environment complexity (such as fast illumination variation, occlusion) and object diversity.
On the basis of traditional object tracking methods, the thesis aims to single object tracking, multi-object tracking, generic object detection and stereo vision object tracking. It combines academic frontiers and presents new algorithms to improve the accuracy and robustness of tracking performance. Moreover, the proposed method is applied to mobile robot object identification, localization and tracking, and it has better tracking performance.
The main research work of this dissertation can be summarized as follows:
(1) A novel object tracking method is presented based on sequential detection mechanism to improve tracking accuracy of single object tracking method in a complex environment. The proposed method integrates particle filter, sparse field active contours and Camshift under sequential detection mechanism. First, particle filter object tracking is applied using color feature to estimate optimal tracking window and then sparse field active contours is performed based on the similarity of object and resulting windows. Similarity detection is carried out again to determine whether Camshift should be employed to modify the object contour and thus achieve accurate tracking of moving object. Experiments demonstrate that the proposed method based on sequential detection mechanism can effectively track and locate the moving object, and it can handle both target changes in scale, orientation, view and environment illumination changes, moreover, it is able to track and locate the object accurately.
(2) A new adaptive kernel-based target tracking method with multiple features fusion is proposed to solve object model based on single visual feature not having enough robustness when environment changing much, A linear weighted combination of three kernel functions of scale-invariant feature transform (SIFT), color and motion features is applied to represent the probability distribution of the tracked target. Appearance and motion features are combined to enhance the target region location stability and accuracy. The size of the tracking window can be adjusted in real time according to the affine transform parameters of the corresponding SIFT couples. The weights of three kernel functions are also adaptively turned according to the scene, in order to extremely excert the function of the features. Experiments demonstrated that the proposed algorithm can track the moving target successfully in different scenarios. Moreover, it can handle target pose, scale, orientation, view and illumination changes, and its performance is better than the classic Camshift algorithm, SIFT tracking method and color SIFT tracking method.
(3) A novel particle filter algorithm based on information shared mechanism is proposed for multiple object tracking and it can improve tracking efficiency. The proposed method combines particle swarm optimization and ant colony optimization to update particles, and thus population information is fully shared. As a result, it can recover particles diversity and increase the precision of the estimation. Moreover, the convergence of this algorithm is analyzed. The results of visual tracking experiment show that the presented algorithm can realize multi-object tracking with fewer particles and its combination tracking performance is better than classic particle filter and the particle filter based on particle swarm optimization, which demonstrates the effectiveness of the proposed algorithm.
(4) It studies generic object detection. In the view of complexity and diversity of generic object, it proposes Boosting generic object detection method with bag-of-words. Boosting method has good detection efficiency, but it has some fault detections due to the diversity and complexity of object. Bag-of-words method has some advantages, such as local patch features, simplicity and robustness, and it has good classification performance of complex object. The proposed method applies bag-of-words to remove the fault detection and to improve the tracking results of Boosting, and thus it achieves high generic object detection accuracy.
(5) For binocular vision hasing abundant information than monocular vision, this thesis focus on binocular vision tracking and it presents two real-time dynamic object recognition and localization methods for mobile robot using binocular vision. For one, firstly, the SIFT operator is applied to object features extraction and object matching with the disparity features of binocular vision. Then the object area is extracted through region growing method. Finally, according to the binocular vision calibration model, the object's location is obtained. For another, it is combined sequential detection scheme, disparity information and binocular vision calibration model to accomplish binocular vision tracking. The disparity confidence interval criterion is introduced to decrease the effect of the noise effectively and enhance the object localization accuracy. Experiments demonstrate that the proposed methods can effectively recognize and locate the dynamic object in both camera moving and object moving.
本文在研究传统视觉目标跟踪算法的基础上,对单目标跟踪、多目标跟踪、广义目标检测、立体视觉跟踪等问题,结合学术前沿知识,提出了新的算法,提高跟踪的准确性和鲁棒性。最后还应用到移动机器人目标识别、定位与跟踪上,具有较好的跟踪性能。
本文研究工作可以总结为以下几个方面：
(1)为了克服单个目标跟踪算法在复杂环境下跟踪精度不高的问题,提出了一种基于序贯检测机制的运动目标跟踪算法。该算法在序贯检测机制下,将粒子滤波、稀疏场主动轮廓和Camshift等算法结合。首先用基于颜色特征的粒子滤波估计最优跟踪窗口;而此跟踪窗口和目标的相似度决定是否采用稀疏场主动轮廓算法,同样目标轮廓和目标的相似度决定是否需要Camshift对轮廓进行修正。跟踪实验表明本章所提的算法在不同的复杂环境和目标有尺度、旋转、视角等姿态变化的情况下,具有较好的跟踪精度和鲁棒性。
(2)针对单一视觉特征的目标模型很难对环境中所有变化都具有足够的鲁棒性,提出了一种自适应多特征融合的核函数目标跟踪算法。该算法目标模型的概率分布用SIFT、颜色和运动特征的核函数线性加权来表示。目标的表征特征和运动特征相结合,可以提高跟踪的稳定性和精确性。跟踪窗口尺寸根据匹配到的SIFT特征对的仿射变化参数实时调整。每个特征核函数的权重跟随特征的显著性变化而自适应变化,从而可以最大限度地发挥每个特征的作用。实验表明该算法可以在不同的场景下跟踪目标,而且可以应对目标有姿态、尺度、旋转、视点变化和环境光照变化等情况,而且本算法的跟踪性能优于Camshift算法、基于SIFT特征目标跟踪、基于彩色SIFT目标跟踪算法。
(3)针对多目标跟踪问题,提出了一种基于信息分享机制的粒子滤波多目标跟踪算法,提高多目标跟踪效率。该算法将粒子群优化算法和蚁群优化算法的优化思想共同作用到粒子更新中,实现粒子之间信息共享,从而增强粒子的多样性和最优估计能力；同时分析了该算法的收敛性。实验表明,本算法能用较少的粒子达到较高的跟踪精度,而且跟踪性能优于传统粒子滤波和基于粒子群优化的粒子滤波算法。
(4)研究了广义目标检测算法,针对广义目标的复杂性和多样性,提出了一种嵌入Bag-of-words的Boosting目标检测算法。Boosting算法具有较好的检测效率,但是对于广义目标的复杂性和多样性,会存在一定的误检测,本文算法嵌入Bag-of-words算法,用其基于局部块特征,而且简单、鲁棒性好,对遮挡、复杂目标具有较好的分类性能等特性,对Boosting检测结果进行修正,剔除其误检测部分,从而提高广义目标检测精度。
(5)针对双目视觉比单目视觉具有更丰富的信息量,研究了基于双目视觉的目标跟踪算法,提出了两种基于双目视觉的移动机器人实时动态目标识别与定位的算法。一种算法首先采用SIFT算法提取目标特征,并结合双目视差特征进行目标匹配；然后通过区域增长算法进行目标区域的提取；最后结合双目视觉标定模型对目标进行定位。另外一种算法把基于序贯检测机制算法、双目视觉视差信息、标定模型相结合来完成双目视觉跟踪,再引入视差置信区间判据可有效减少噪声影响,提高运动目标定位精度。实验表明这两种算法在摄像机运动-目标运动情况下,能对动态目标进行有效地识别与定位。
Dynamic target tracking is always given to attention for computer vision, and it is widely applied in robot location and navigation, multi-robot formation, lunar exploration and intelligent surveillance. Significant progress has been made in targets tracking during the last few years. However, the task of robust tracking is challenging due to the environment complexity (such as fast illumination variation, occlusion) and object diversity.
On the basis of traditional object tracking methods, the thesis aims to single object tracking, multi-object tracking, generic object detection and stereo vision object tracking. It combines academic frontiers and presents new algorithms to improve the accuracy and robustness of tracking performance. Moreover, the proposed method is applied to mobile robot object identification, localization and tracking, and it has better tracking performance.
The main research work of this dissertation can be summarized as follows:
(1) A novel object tracking method is presented based on sequential detection mechanism to improve tracking accuracy of single object tracking method in a complex environment. The proposed method integrates particle filter, sparse field active contours and Camshift under sequential detection mechanism. First, particle filter object tracking is applied using color feature to estimate optimal tracking window and then sparse field active contours is performed based on the similarity of object and resulting windows. Similarity detection is carried out again to determine whether Camshift should be employed to modify the object contour and thus achieve accurate tracking of moving object. Experiments demonstrate that the proposed method based on sequential detection mechanism can effectively track and locate the moving object, and it can handle both target changes in scale, orientation, view and environment illumination changes, moreover, it is able to track and locate the object accurately.
(2) A new adaptive kernel-based target tracking method with multiple features fusion is proposed to solve object model based on single visual feature not having enough robustness when environment changing much, A linear weighted combination of three kernel functions of scale-invariant feature transform (SIFT), color and motion features is applied to represent the probability distribution of the tracked target. Appearance and motion features are combined to enhance the target region location stability and accuracy. The size of the tracking window can be adjusted in real time according to the affine transform parameters of the corresponding SIFT couples. The weights of three kernel functions are also adaptively turned according to the scene, in order to extremely excert the function of the features. Experiments demonstrated that the proposed algorithm can track the moving target successfully in different scenarios. Moreover, it can handle target pose, scale, orientation, view and illumination changes, and its performance is better than the classic Camshift algorithm, SIFT tracking method and color SIFT tracking method.
(3) A novel particle filter algorithm based on information shared mechanism is proposed for multiple object tracking and it can improve tracking efficiency. The proposed method combines particle swarm optimization and ant colony optimization to update particles, and thus population information is fully shared. As a result, it can recover particles diversity and increase the precision of the estimation. Moreover, the convergence of this algorithm is analyzed. The results of visual tracking experiment show that the presented algorithm can realize multi-object tracking with fewer particles and its combination tracking performance is better than classic particle filter and the particle filter based on particle swarm optimization, which demonstrates the effectiveness of the proposed algorithm.
(4) It studies generic object detection. In the view of complexity and diversity of generic object, it proposes Boosting generic object detection method with bag-of-words. Boosting method has good detection efficiency, but it has some fault detections due to the diversity and complexity of object. Bag-of-words method has some advantages, such as local patch features, simplicity and robustness, and it has good classification performance of complex object. The proposed method applies bag-of-words to remove the fault detection and to improve the tracking results of Boosting, and thus it achieves high generic object detection accuracy.
(5) For binocular vision hasing abundant information than monocular vision, this thesis focus on binocular vision tracking and it presents two real-time dynamic object recognition and localization methods for mobile robot using binocular vision. For one, firstly, the SIFT operator is applied to object features extraction and object matching with the disparity features of binocular vision. Then the object area is extracted through region growing method. Finally, according to the binocular vision calibration model, the object's location is obtained. For another, it is combined sequential detection scheme, disparity information and binocular vision calibration model to accomplish binocular vision tracking. The disparity confidence interval criterion is introduced to decrease the effect of the noise effectively and enhance the object localization accuracy. Experiments demonstrate that the proposed methods can effectively recognize and locate the dynamic object in both camera moving and object moving.
引文
[1]Ramesh Jain R K, Brian G.Schunck. Machine Vision[M]. McGraw-Hill,2003.
[2]贾云德.机器视觉[M].北京：科学出版社,2000.
[3]Tippett James T, Borkowitz David A, Clapp Lewis C, et al. Optical and electro-optical information processing[M]. Cambridge, Massachusetts:Massachusetts Institute of Technology Press,1965.
[4]Marr D,姚国正等译.视觉计算理论[M].北京：科学出版社,1988.
[5]Barrow H G, Tenenbaum J M. Computational vision[J]. Proceedings of the IEEE.1981, 69(5):572-595.
[6]Horn B K P. Robot Vision[M]. Cambridge, Massachusetts:Massachusetts Institute of Technology Press,1986.
[7]Woodham RJ. Reflectance map techniques for analyzing surface defects in Metal casting[R]. Technical Report 457, MIT AI Lab.1978.
[8]Xu D, Calderron C A, Gan J Q. An analysis of the inverse kinematics for a 5-DOF manipulator [J]. International Journal of Automation and Computing.2005,2(2): 114-124.
[9]赵清杰,连广宇,孙增圻.机器人视觉伺服综述[J].控制与决策,2001,16(6)：849-853.
[10]Chaumette F, Hutchinson S. Visual servo control. I. basic approaches [J]. IEEE Robotics & Automation Magazine.2006,13(4):82-90.
[11]Lazar C, Burlacu A. Dynamic simulation model for image based visual servo control systems[C]. Proceedings of International conference on Optimization of Electrical and Electronic Equipment.2008:185-190.
[12]Koenig T, Dong Y, DeSouza G N. Image-based visual servoing of a real robot using a quaternion formulation[C]. Proceedings of IEEE International Conference on Robotics, Automation and Mechatronics.2008:216-221.
[13]Lidai Wang, Mills J K, Cleghorn W L. Automatic microassembly using visual servo control[J]. IEEE Transaction on Electronics Packaging Manufacturing.2008,31(4): 316-325.
[14]Sahin T, Zergeroglu E. Adaptive visual servo control of robot manipulators via composite camera inputs [C]. Proceedings of International Workshop on Robot Motion and Control.2005:219-224
[15]Gowdy J. Emergent architectures:a case study for outdoor mobile robots[D]. Robotics Institute, Carnegie Mellon University,2000.
[16]Thrun S, Montemerlo M, Dahlkamp H, et al. Stanley:the robot that won the DARPA grand challenge[J]. Journal of Field Robotics.2006,23(9):661-692.
[17]David S T, Ethan T T. Human-Robot Interaction. Carnegie Mellon Spring,2009.
[18]Nickel K, Stiefelhagen R. Real-time person tracking and pointing gesture recognition for human-robot interaction[J]. Lecture Notes in Computer Science.2004,3058:28-38.
[19]Song K T, Chen W J. Face recognition and tracking for human-robot interaction[C]. Proceedings of IEEE International Conference on Systems, Man and Cybernetics.2004, 3:2877-2882.
[20]Atienza R, Zelinsky A. Active gaze tracking for human-robot interaction[C]. Proceedings of the 4th IEEE International Conference on Multimodal Interfaces.2002: 261-266.
[21]Kanda T, Ishiguro H. Body movement analysis of human-robot interaction[C]. Proceedings of the 18th International Joint Conference on Artificial Intelligence. 2003:177-182.
[22]Cruz C, Sucar L E, Morales E F. Real-time face recognition for human-robot interaction[C]. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition.2008:1-6.
[23]Barreto J, Menezes P, Dias J. Human-robot interaction based on haar-like features and eigenfaces[C]. Proceedings of IEEE International Conference on Robotics and Automation.2004,2:1888-1893.
[24]Alper Y, Omar J, Mubarak S. Object tracking:A survey [J]. ACM Computing Surverys. 2006,38(4):1-45.
[25]侯志强,韩崇昭.视觉跟踪技术综述[J].自动化学报.2006,32(4)：603-617.
[26]Anderson C, Burt P, Van Der Wal G. Change detection and tracking using pyramid transform techniques [C]. Proceedings of SPIE-Intelligent Robots and Computer Vision. 1985:300-305.
[27]Chen L F, Liao H Y M, Lin J C. Wavelet-based optical flow estimation[J]. IEEE Transactions on Circuits and Systems for Video Technology.2002,12(1):1-12.
[28]Amiaz T, Kiryati N. Dense discontinuous optical flow via contour-based segmentation[C]. Proceedings of IEEE International Conference on Image Processing 2006,3:1264-1267.
[29]Mittal A, Paragios N. Motion-based background subtraction using adaptive kernel density estimation[C]. Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2004,2:302-309.
[30]Meyer F G, Bouthemy P. Region-based tracking using affine motion models in long image sequences[J]. Computer Vision and Image Understanding.1994,60(2):119-140.
[31]Bascle B, Deriche R. Region tracking through image sequences[C]. Proceedings of IEEE International Conference on Computer Vision.2002:302-307.
[32]Birchfield S T, Rangarajan S. Spatiograms versus histograms for region-based tracking[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition.2005:1158-1163
[33]Matthews L, Ishikawa T, Baker S. The template update problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(6):810-815.
[34]Avidan S. Support vector tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(8):1064-1072.
[35]Gang L, Yanghai T, Genc Y. Exploiting occluding contours for real-time 3D tracking:a unified approach[C]. Proceeding of IEEE 11th International Conference on Computer Vision.2007:1-8.
[36]Prisacariu V A, Timofte R, Zimmermann K, et al. Integrating object detection with 3D tracking towards a better driver assistance system[C]. Proceedings of 20th International Conference on Pattern Recognition.2010:3344-3347.
[37]邵文坤.便携式地面移动机器人目标跟踪方法研究[D].国防科学技术大学硕士学位论文,2005.
[38]安建虎.相关跟踪与特征匹配技术研究[D].中国科学院沈阳自动化研究所,2002.
[39]Michael Kass, Andrew Witkin D T. Snake:Active contour models[J]. International Journal of Computer Vision.1988,1(4):321-331.
[40]Menet S, Saint-Marc P, Mediom G. B-snakes:implementation and application to stereo[C]. Proceedings of the DARPA Image Understanding Workshop.1991:720-726.
[41]Kim W, Lee C Y, Lee J J. Tracking moving object using Snake's jump based on image flow[J]. Mechatronics.2001,11(2):199-226.
[42]Niethammer M, Tannenbaum A, Angenent S. Dynamic active contours for visual tracking[J]. IEEE Transactions on Automatic Control.2006,51(4):562-579.
[43]董春利,董育宁,刘杰.基于粒子滤波和GVF-Snake的目标跟踪算法[J].仪器仪表学报.2009,30(4)：828-833.
[44]Mitra P, Murthy C, Pal S K. Unsupervised feature selection using feature similarity [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2002,24(3):301-312.
[45]Kaneko T, Hori O. Feature selection for reliable tracking using template matching [C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2003:796-802.
[46]Kloihofer W, Kampel M. Interest point based tracking[C]. Proceedings of the 20th International Conference on Pattern Recognition.2010:3549-3552.
[47]Hofhauser A, Steger C, Navab N. Edge-Based template matching and tracking for perspectively distorted planar objects[M]. springer,2008.
[48]Zhang X, Yang J. The analysis of the color similarity problem in moving object detection[J]. Signal Processing.2009,89(4):685-691.
[49]Wang J, Yagi Yasushi. Integrating color and shape-texture words for adaptive real-time object tracking[J]. IEEE Transactions on Image Processing.2008,17(2):235-240.
[50]Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003:564-575.
[51]Gary R. Bradski. Computer vision face tracking for use in a perceptual user interface[J]. Intel Technology Journal Q2'98.1998:1-15.
[52]Wang Z, Yang X, Xu Y, et al. Camshift guided particle filter for visual tracking[J]. Pattern Recognition Letters.2009,30(4):407-413.
[53]Shaik Z, Asari V. A robust method for multiple face tracking using Kalman filter[C]. Proceedings of The 36th IEEE Applied Imagery Pattern Recognition Workshop.2008: 125-130.
[54]Kalkan S, Calow D, Wrgtter F, et al. Local image structures and optic flow estimation[J]. Network:Computation in Neural Systems.2005,16(4):341-356.
[55]Amiaz T, Kiryati N. Dense discontinuous optical flow via contour-based segmentation[C]. Proceedings of IEEE International Conference on Image Processing. 2006,3:1264-1267.
[56]查宇飞,张育,毕笃彦.基于区域活动轮廓运动目标跟踪方法研究[J].中国图象图形学报.2006,11(12)：1844-1848.
[57]Pang Y, Huang Q, Zhang W, et al. Real-time object tracking of a robot head based on multiple visual cues integration[C]. Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.2006:686-691.
[58]王宾,潘建寿,王琳等.基于多特征融合的均值偏移视频目标跟踪算法[J].小型微型计算机系统.2006,27(9)：1746-1749.
[59]孙凯.基于视觉的移动机器人运动目标跟踪研究[D].杭州电子科技大学,2009.
[60]Kung C, Kung C, Wang J. High performance real-time object detecting and tracking system for multiple moving targets[C]. Proceedings of IEEE Conference on Instrumentation and Measurement Technology.2007:1-4.
[61]Li T H S, Chang S J, Tong W. Fuzzy target tracking control of autonomous mobile robots by using infrared sensors[J]. IEEE Transactions on Fuzzy Systems.2004,12(4): 491-501.
[62]Guo X, Wang C, Qu Z. Object Tracking for Autonomous Mobile Robot based on Feedback of Monocular-Vision[C]. Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications.2007:467-470.
[63]Chen C H, Cheng C, Page D, et al. A moving object tracked by a mobile robot with real-time obstacles avoidance capacity[J]. Pattern Recognition.2006,3:1091-1094.
[64]梁冰,洪炳镕,曙光.基于视觉与行为模型的机器人目标跟踪[J].通信学报.2004,25(1)：92-99.
[65]Meng Y, Liu X, Liang Z. A robust method for mobile robot tracking object on behavior-based robotics[C]. Proceedings of the 1st IEEE International Symposium on Information Technologies and Applications in Education 2007:468-472.
[66]Kye Kyung K, Soo Hyun C, Hae Jin K, et al. Detecting and tracking moving object using an active camera[C]. Proceedings of the 7th International Conference on Advanced Communication Technology.2005,2:817-820.
[67]Asada M, Tanaka T, Hosoda K. Adaptive binocular visual servoing for independently moving target tracking[C]. Proceeding of IEEE International Conference on Roboties and Automation.2000,3:2076-2081.
[68]Weber J, Koller D, Luong Q T, et al. New results in stereo-based automatic vehicle guidance[C]. Proeeedings of the Intelligent Vechiles 95 Symposium.1995:530-535.
[69]Fang Y, Masaki I, Horn B. Depth-based target segmentation for intelligent vehicles: fusion of radar and binocular stereo [J]. IEEE Transactions on Intelligent Transportation Systems.2002,3(3):196-202.
[70]Tsalatsanis A, Valavanis K, Yalcin A. Vision based target tracking and collision avoidance for mobile robots[J]. Journal of Intelligent and Robotic Systems.2007,48(2): 285-304.
[71]Sanghoon K, Sangmu L, Seungjong K. Object tracking of mobile robot using moving color and shape information for the aged walking [C]. Proceedings of the 2nd International Conference on Future Generation Communication and Networking.2008, 2:293-297.
[72]高庆吉,洪炳熔,阮玉峰.基于异构双目视觉的全自主足球机器人导航[J].哈尔滨工业大学学报.2003,35(9)：1029-1032.
[73]祝琨.基于双目视觉信息的运动物体实时跟踪与测距[D].北京交通大学,2008.
[74]Buehler M, Iagnemma K, Singh S. The 2005 DARPA grand challenge:The great robot race[M]. Springer Verlag,2007.
[75]Urmson C, Anhalt J, Bagnell D, et al. Autonomous driving in urban environments:Boss and the urban challenge[J]. Journal of Field Robotics.2008,25(8):425-466.
[76]Betge-Brezetz S, Chatila R, Devy M. Natural scene understanding for mobile robot navigation[C]. Proceedings of IEEE International Conference on Robotics and Automation.1994,1:730-736.
[77]王璐,陆筱霞,蔡自兴.基于局部显著区域的自然场景识别[J].中国图象图形学报.2008,13(8)：1594-1600.
[78]张敏,刘利雄,贾云得.一种基于图像区域系综分类的室外场景理解方法[J].中国图象图形学报.2004,9(2)：1443-1448.
[79]Huhns M, Ruvinsky A, McCants D. The JIDOKA system for multiple-sensor terrain classification[R]. Technical Report CSE TR-2006-13, Center for Information Technology, University of South Carolina, USA,2006.
[80]Bosch A, Munoz X, Freixenet J. Segmentation and description of natural outdoor scenes[J]. Image and Vision Computing.2007,25(5):727-740.
[81]Posner I, Cummins M, Newman P. A generative framework for fast urban labeling using spatial and temporal context[J]. Autonomous Robots.2009,26(2):153-170.
[82]Li L J, Li F F. What, where and who? classifying event by scene and object recognition[C]. Proceedings of IEEE 11th International Conference on Computer Vision.2007:1-8.
[83]Li L J, Socher R, Li F F. Towards total scene understanding:Classification, annotation and segmentation in an automatic framework [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2009:2036-2043.
[84]Wang C, Blei D, Li F F. Simultaneous image classification and annotation[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2009: 1903-1910.
[85]许海霞,王耀南.一种分层Mean Shift目标跟踪算法[J].自动化学报.2009,35(4)：401-409.
[86]Zhong Y, Jain A K, Dubuisson-Jolly M P. Object tracking using deformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000, 22(5):544-549.
[87]Doucet A, Gordon Nei, Krishnamurthy. Particle filters for state estimation of jump Markov linear systems[J]. IEEE Transaction on Signal Processing.2001,49(3):613-624
[88]Jing L, Vadakkepat P. Interacting MCMC particle filter for tracking maneuvering target[J]. Digital Signal Processing.2010,20(2):561-574.
[89]Chung C Y, Chen H H. Video object extraction via MRF-based contour tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology.2010,20(1): 149-155.
[90]Wu M, Peng X, Zhang Q, et al. Patches-based Markov random field model for multiple object tracking under occlusion[J]. Signal Processing.2010,90(5):1518-1529.
[91]Maggio E, Piccardo E, Regazzoni C, et al. Particle PHD filtering for multi-target visual tracking [C]. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing.2007,1:1101-1104.
[92]Suga A, Fukuda K, Takiguchi T, et al. Object recognition and segmentation using SIFT and Graph Cuts[C]. Proceedings of the 19th International Conference on Pattern Recognition.2008:1-4.
[93]Lee D S. Effective gaussian mixture learning for video background subtraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(5):827-832.
[94]Nummiaro K, Koller-Meier E, Van Gool L. An adaptive color-based particle filter[J]. Image and Vision Computing.2003,21(1):99-110.
[95]Hue C L, Perez P. Tracking multiple objects with particle filtering[J]. IEEE Transactions on Aerospace and Electronic Systems.2002,38(3):791-812.
[96]Okuma K T, De Freitas N, Little J J, et al. A boosted particle filter:multitarget detection and tracking[J]. Lecture Notes in Computer Science.2004,3021:28-39.
[97]MacCormick J, F M. Stochastic algorithms for visual tracking[M]. Londen:Springer, 2002.
[98]Rudolph van der Merwe. The unscented particle filter[D]. Cambridge:Cambridge University.2000.
[99]Cheng H Y, Hwang J N. Adaptive particle sampling and adaptive appearance for multiple video object tracking [J]. Signal Processing.2009,89(9):1844-1849.
[100]方正,佟国峰,徐心和.粒子群优化粒子滤波方法[J].控制与决策.2007,22(3)：273-277.
[101]Zheng Y, Meng Y. Swarming particles with multi-feature model for free-selected object tracking[C]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.2008:2553-2558.
[102]Zhang X, Hu W, Maybank S, et al. Sequential particle swarm optimization for visual tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2008:1-8.
[103]Riklin Raviv T, Kiryati N, Sochen N. Prior-based segmentation by projective registration and level sets[C]. Proceedings of the10th IEEE International Conference on Computer Vision.2005,1:204-211.
[104]Michailovich O, Rathi Y, Tannenbaum A. Image segmentation using active contours driven by the bhattacharyya gradient flow[J]. IEEE Transactions on Image Processing. 2007,16(11):2787-2801.
[105]Isard M, Blake A. Condensation-conditional density propagation for visual tracking[J]. International Journal of Computer Vision.1998,29(1):5-28.
[106]Isard M, Blake A. Condensation:unifying low-level and high-level tracking in a stochastic framework[C]. Proceedings of the 5th European Conference on Computer Vision.1998:893-908.
[107]Rathi Y, Vaswani N, Tannenbaum A, et al. Tracking deforming objects using particle filtering for geometric active contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2007,29(8):1470-1475.
[108]李培华.主动轮廓线模型(蛇模型)综述[J].软件学报.2000,11(6)：751-757.
[109]Xu N, Ahuja N, Bansal R. Object segmentation using graph cuts based active contours[J]. Computer Vision and Image Understanding.2007,107(3):210-224.
[110]Mukherjee D P, Ray N, Acton S T. Level set analysis for leukocyte detection and tracking[J]. IEEE Transactions on Image Processing.2004,13(4):562-572.
[111]Xu C Y, Yezzi A Jr, Prince J L. On the relationship between parametric and geometric active contours[C]. Proceedings of the 43rd Asilomar Conference on Signals, Systems and Computers.2000,1:483-489
[112]张丽飞,王东峰.基于形变模型的图像分割技术综述[J].电子与信息学报2003,25(3)：395-403.
[113]Tannenbaum A, Angenent S. Dynamic Active Contours for Visual Tracking[J]. IEEE Transactions on Automatic Control.2006,51(4):562-579.
[114]Osher S, Sethian J. Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi formulations [J]. Journal of Computational Physics.1988, 79(1):12-49.
[115]Richard Tsai, Stanley O. Level set methods and their applications in image science[J]. Communications in Mathematical Sciences.2004,1(4):1-20.
[116]Adalsteinsson D, Sethian J A. A fast level set method for propagating interfaces[J]. Journal of Computational Physics.1995,118(2):269-277.
[117]Whitaker R T. A level-set approach to 3d reconstruction from range data[J]. International Journal of Computer Vision.1998,29(3):203-231.
[118]翁建广,庄越挺,潘云鹤.基于改进稀疏场算法的水平集形状过渡[J].软件学报.2006,17(7)：1544-1552.
[119]Comaniciu D, Meer P. Mean Shift:A robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24 (5):603-619.
[120]Comaniciu D. An algorithm for data-driven bandwidth selection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(2):281-288.
[121]Arulampalam M S, Maskell S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing. 2002,50(2):174-188.
[122]Jamasbi B, Motamedi S A, Behrad A. Tracking vehicle targets with large aspect change[C]. Proceedings of IEEE Workshop on Motion and Video Comouting.2007: 22-22.
[123]彭宁高,杨杰,刘志Mean-Shift跟踪算法中核函数窗宽的自动选取[J].软件学报.2004,16(9)：1542-1550.
[124]钱惠敏,茅耀斌,王执铨.自动选择跟踪窗尺度的Mean-Shift算法[J].中国图象图形学报.2007,12(2)：245-249.
[125]Li Z, Chen J, Schraudolph N N. An improved mean-shift tracker with kernel prediction and scale optimisation targeting for low-frame-rate video tracking [C]. Proceedings of the19th International Conference on Pattern Recognition.2008:1-4.
[126]Hu J S, Juan C W, Wang J J. A spatial-color mean-shift object tracking algorithm with scale and orientation estimation[J]. Pattern Recognition Letters.2008,29(16): 2165-2173.
[127]Liu H, Yu Z, Zha H, et al. Robust human tracking based on multi-cue integration and mean-shift[J]. Pattern Recognition Letters.2009,30(9):827-837.
[128]Kim J S, Hong K S. Color-texture segmentation using unsupervised graph cuts[J]. Pattern Recognition.2009,42(5):735-750.
[129]顾建栋,刘志,张兆杨.结合核密度估计和边缘信息的运动对象分割算法[J].计算机辅助设计与图形学学报.2009,21(2)：223-228.
[130]Lowe D G. Distinctive image words from scale-invariant keypoints[J]. International Journal of Computer Vision.2004,60(2):91-110.
[131]Zhou H, Yuan Y, Shi C. Object tracking using SIFT words and mean shift[J]. Computer Vision and Image Understanding.2009,113(3):345-352.
[132]厉茂海,洪炳镕,罗荣华.基于单目视觉的移动机器人全局定位[J].机器人.2007,29(2)：140-144.
[133]吕强,周文晖,刘济林.基于SIFT特征提取的单目视觉里程计在导航系统中的实现[J].传感技术学报.2007,20(5)：1148-1152.
[134]明安龙,马华东.多摄像机之间基于区域SIFT描述子的目标匹配[J].计算机学报.2008,31(4)：650-661.
[135]Yan K, Sukthankar R. PCA-SIFT:a more distinctive representation for local image descriptors[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2004,2:506-513.
[136]Mikolajczyk K, Schmid C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(10):1615-1630.
[137]Burghouts G J, Geusebroek J M. Performance evaluation of local colour invariants [J]. Computer Vision and Image Understanding.2009,113(1):48-62.
[138]Shan X, Wang Y. The matching method based on RANSAC algorithm for estimation of the fundamental matrix[J]. Journal of System Simulation.2006,12(17):2896-2900.
[139]Tuo H, Jing Z, Zhang T. Aerial sequence image mosaic using reduced sift descriptors[C]. Proceedings of International Conference on Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition.2007,6786: 1-9.
[140]Zhao T, Nevatia R. Tracking multiple humans in complex situations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(9):1208-1221.
[141]Yu T, Wu Y. Collaborative tracking of multiple targets[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2004,1:834-841.
[142]McKenna S J, Jabri S, Duric Z, et al. Tracking groups of people[J]. Computer Vision and Image Understanding.2000,80(1):42-56.
[143]Zhang L, Li Y, Nevatia R. Global data association for multi-object tracking using network flows[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2008:1-8.
[144]Fortmann T E, Barshalom Y, Scheffe M. Multi-Target tracking using joint probabilistic data association[C]. Proceedings of the 9th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.1980:807-812.
[145]Reid D. An algorithm for tracking multiple targets[J]. IEEE Transaction on Automat Control.1979,24(6):843-854.
[146]Karlsson R, Gustafsson F. Monte Carlo data association for multiple target tracking[C]. Proceedings of IEE International Seminar on Target Tracking:Algorithmsand Applications.2002,1:13-17.
[147]Schulz D, Burgard W, Fox D, et al. Tracking multiple moving targets with a mobile robot using particle filters and statistical data association[C]. Proceedings of IEEE International Conference on Robotics and Automation.2001,2:1665-1670.
[148]Sarkka S, Vehtari A, Lampinen J. Rao-Blackwellized Monte Carlo data association for multiple target tracking[C]. Proceedings of the 7th International Conference on Information Fusion.2004,7:583-590.
[149]Smith K, Gatica-Perez D, Odobez J M. Evaluating Multi-Object Tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2005, 36-43.
[150]梁敏,刘贵喜.基于自适应混合滤波的多目标跟踪算法[J].光学学报.2010,30(9)：2554-2561.
[151]刘国成,王永骥.一种基于改进粒子滤波的多目标跟踪算法.控制与决策.2009,24(2)：317-320
[152]Perera A, Srinivas C, Hoogs A. Multi-Object tracking through simultaneous long occlusions and split-merge conditions [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2006,1:666-673.
[153]Liu J S, Chen R. Sequential Monte Carlo methods for dynamic systems[J]. Journal of the American Statistical Association.1998,93(443):1032-1044.
[154]杨小军,潘泉,王睿.粒子滤波进展与展望[J].控制理论与应用.2006,23：261-267.
[155]Pitt M K, Shephard N. Filtering via simulation:Auxiliary particle filters[J]. Journal of the American Statistical Association.1999,94(2):590-599.
[156]Johnston C M, Mould N, Havlicek J P, et al. Dual domain auxiliary particle filter with integrated target signature update[C]. Proceedings of IEEE Conference on Computer Vision and Pattern.2009:54-59.
[157]Chen Z, Haykin S. On different facets of regularization theory [J]. Neural Computation. 2002,14(12):2791-2846.
[158]Antonacci F, Matteucci M, Migliore D, et al. Tracking multiple acoustic sources in reverberant environments using regularized particle filter[C]. Proceedings of International Conference on Digital Signal Processing.2007:99-102.
[159]Fox D. Adapting the sample size in particle filters through KLD-sampling[J]. The International Journal of Robotics Research.2003,22(12):985-1003.
[160]Liang Z, Ma X, Dai X. Robust tracking of moving sound source using scaled unscented particle filter[J]. Applied Acoustics.2008,69(8):673-680.
[161]Song C, Zhao H, Jing W, et al. A new particle swarm optimization based unscented particle filtering[C]. Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering.2009:1-4.
[162]Kotecha J H, Djuric P M. Gaussian particle filtering[J]. IEEE Transactions on Signal Processing.2003,51(10):2592-2601.
[163]Kotecha J H, Djuri P M. Blind sequential detection for Rayleigh fading channels using hybrid Monte Carlo-recursive identification algorithms[J]. Signal Processing.2004, 84(5):825-832.
[164]Bhaskar H, Mihaylova L, Maskell S. Population based particle filtering[C]. Proceedings of 2008 IET Seminar on Target Tracking and Data Fusion:Algorithms and Applications. 2008:31-38.
[165]张琪,王鑫,胡昌华.人工免疫粒子滤波算法的研究[J].控制与决策.2008,23(3)：293-301.
[166]Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics. 2002,26(1):29-41.
[167]吕强,刘士荣,邱雪娜.基于信息素机制的粒子群优化算法的设计与实现[J].自动化学报.2009,35(11)：1410-1419.
[168]方正,佟国峰,徐心和.基于粒子群优化的粒子滤波定位方法[J].控制理论与应用.2008,25(3)：533-534.
[169]Kenndy J, Eberhart R. Particle swarm optimization[C]. Proceedings of IEEE International Conference on Neural Networks.1995:1941-1948.
[170]Crisan D, Doucet A. A survey of convergence results on particle filtering methods for practitioners[J]. IEEE Transactions on Signal Processing.2002,50(3):736-746.
[171]Collins R T, Liu Y, Leordeanu M. Online selection of discriminative tracking words[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005:1631-1643
[172]Tang F, Brennan S, Zhao Q, et al. Co-tracking using semi-supervised support vector machines[C]. Proceedings of IEEE Conference on Computer Vision.2007:1-8.
[173]Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision.2008,77(1):125-141.
[174]Nguyen Dang B. A robust framework for visual object tracking[C]. Proceedings of International Conference on Computing and Communication Technologies.2009:1-8.
[175]席涛,张胜修,颜诗源.基于在线学习的自适应粒子滤波视频目标跟踪[J].光电工程.2010,37(6)：29-34.
[176]高琳,唐鹏,盛鹏.复杂场景下基于条件随机场的视觉目标跟踪[J].光学学报.2010,30(6)：1721-1728.
[177]温静.基于张量子空间学习的视觉跟踪方法研究[D].西安电子科技大学,2010.
[178]张辉,赵保军.基于概率主成分分析表观模型的视觉跟踪[J].系统工程与电子技术.2010,31(12)：2826-2829.
[179]Fukun B, Mingming B, Feng L, et al. A local descriptor based model with visual attention guidance for generic object detection[C]. Proceedings of the 3rd International Congress on Image and Signal Processing.2010,4:1599-1604.
[180]Tom Mitchell. Machine Learning. New York:McGraw Hill,1997.
[181]Tu Z. Probabilistic boosting-tree:Learning discriminative models for classification, recognition, and clustering[C]. Proceedings of IEEE Conference on Computer Vision. 2005,2:1589-1596.
[182]Serre T, Wolf L, Bileschi S, et al. Robust object recognition with cortex-like mechanisms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2007: 411-426.
[183]张骏,高隽,谢昭等.基于统计分析Boosting的复杂场景目标识别方法研究[J].仪器仪表学报.2010,31(8)：1788-1795.
[184]Ali S, Shah M. An integrated approach for generic object detection using kernel PCA and boosting[C]. Proceedings of IEEE International Conference on Multimedia and Expo.2005:1-4.
[185]Ali S, Shah M. A supervised learning framework for generic object detection in images [C]. Proceedings of the 10th IEEE International Conference on Computer Vision. 2005,2:1347-1354.
[186]Zaidi N A, Suter D. Confidence rated boosting algorithm for generic object detection[C]. Proceedings of the 19th International Conference on Pattern Recognition. 2008:1-4.
[187]Schapire R E. The strength of weak learnability[J]. Machine Learning.1990,5(2): 197-227.
[188]Freund Y. Boosting a weak learning algorithm by majority[J]. Information and Computation.1995,121(2):256-285.
[189]Schapire Y, Schapire F E. A decision-theoretic generalization of online learning and an application to boosting[J]. Journal of Computer and System Sciences.1997,55(1): 119-139.
[190]Freund Y, Schapire R E. Experiments with a new boosting algorithm[C]. Proceedings of the 13th International Conference on Machine Learning.1996:148-156.
[191]Schapire R E, Singer Y. Improved boosting algorithms using confidence-rated predictions[J]. Machine Learning.1999,37(3):297-336.
[192]Torralba A. A simple object detector with boosting[C]. Proceedings of ICCV short courses on Recognizing and Learning Object Categories.2005,1-5.
[193]Friedman J, Hastie T, Tibshirani R. Additive logistic regression:a statistical view of boosting[J]. The Annals of Statistics.2000,28(2):337-407.
[194]Csurka G, Dance C, Fan L, et al. Visual categorization with bags of keypoints[C]. Proceedings of ECCV Workshop on Statistical Learning for Computer Vision.2004,1: 59-74.
[195]Li F F, Pietro P. A bayesian hierarchical model for learning natural scene categories[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2005,2: 1-8.
[196]Sivic J, Russell B C, Efros A A, et al. Discovering object categories in image collections[C]. Proceedings of the IEEE International Conference on Computer Vision. 2005:1-8.
[197]Robert F, Li Feifei. Learning object categories from Google's image search[C]. Proceedings of IEEE International Conference on Computer Vision.2005,2: 1816-1823.
[198]Hofmann T. Unsupervised learning by probabilistic latent semantic analysis[J]. Machine learning.2001,42(1):177-196.
[199]Sivic J, Russell B C, Efros A A, et al. Discovering objects and their location in images [C]. Proceedings of the 10th International Conference on Computer Vision.2005, 1:370-377.
[200]Bryan Russell A T a W T. http://labelme.csail.mit.edu/,2009.
[201]邓汉成,王敏芳,王瑛.查全率与查准率之间关系的理论研究[J].情报学报.2000,19(4)：359—362
[202]Makhoul J, Kubala F, Schwartz R. Performance measures for information extraction[C]. Proceedings of DARPA Broadcast News Workshop.1999,249-252.
[203]Olson C F, Matthies L H. Maximum likelihood rover localization by matching range maps[C]. Proceedings of the IEEE International Conference on Robotics and Automation.2002,1:272-277.
[204]祝琨,杨唐文,阮秋琦等.基于双目视觉的运动物体实时跟踪与测距[J].机器人.2009,31(4)：327-334.
[205]Munoz Salinas R, Aguirre E, Garcia Silvente M, et al. A multiple object tracking approach that combines colour and depth information using a confidence measure [J]. Pattern Recognition Letters.2008,29(10):1504-1514.
[206]Faig W. Calibration of close-range photogrammetric system:mathmatical formulation [J]. Photogrammetric Engineering and Remote Sensing.1976,41(12): 1479-1486.
[207]Tsai R. An efficient and accurate camera calibration technique for 3D machine vision[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.1986:364-374.
[208]Tsai R. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE Journal of Robotics and Automation.1987,3(4):323-344.
[209]Weng J, Cohen P, Herniou M. Camera calibration with distortion models and accuracy evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2002, 14(10):965-980.
[210]Martins H, Birk J, Kelley R. Camera models based on data from two calibration planes[J]. Computer Graphics and Image Processing.1981,17(2):173-180.
[211]孟晓桥.摄像机自标定和一维重建中的若干问题研究[D].中国科学院,2000.
[212]崔彦平,林玉池.基于神经网络的双目视觉摄像机标定方法的研究[J].光电子.激光.2005,16(9)：1097-1100.
[213]傅其凤,崔彦平.双目视觉摄像机神经网络标定方法[J].工程图学学报.2005,6：93-97.
[2]贾云德.机器视觉[M].北京：科学出版社,2000.
[3]Tippett James T, Borkowitz David A, Clapp Lewis C, et al. Optical and electro-optical information processing[M]. Cambridge, Massachusetts:Massachusetts Institute of Technology Press,1965.
[4]Marr D,姚国正等译.视觉计算理论[M].北京：科学出版社,1988.
[5]Barrow H G, Tenenbaum J M. Computational vision[J]. Proceedings of the IEEE.1981, 69(5):572-595.
[6]Horn B K P. Robot Vision[M]. Cambridge, Massachusetts:Massachusetts Institute of Technology Press,1986.
[7]Woodham RJ. Reflectance map techniques for analyzing surface defects in Metal casting[R]. Technical Report 457, MIT AI Lab.1978.
[8]Xu D, Calderron C A, Gan J Q. An analysis of the inverse kinematics for a 5-DOF manipulator [J]. International Journal of Automation and Computing.2005,2(2): 114-124.
[9]赵清杰,连广宇,孙增圻.机器人视觉伺服综述[J].控制与决策,2001,16(6)：849-853.
[10]Chaumette F, Hutchinson S. Visual servo control. I. basic approaches [J]. IEEE Robotics & Automation Magazine.2006,13(4):82-90.
[11]Lazar C, Burlacu A. Dynamic simulation model for image based visual servo control systems[C]. Proceedings of International conference on Optimization of Electrical and Electronic Equipment.2008:185-190.
[12]Koenig T, Dong Y, DeSouza G N. Image-based visual servoing of a real robot using a quaternion formulation[C]. Proceedings of IEEE International Conference on Robotics, Automation and Mechatronics.2008:216-221.
[13]Lidai Wang, Mills J K, Cleghorn W L. Automatic microassembly using visual servo control[J]. IEEE Transaction on Electronics Packaging Manufacturing.2008,31(4): 316-325.
[14]Sahin T, Zergeroglu E. Adaptive visual servo control of robot manipulators via composite camera inputs [C]. Proceedings of International Workshop on Robot Motion and Control.2005:219-224
[15]Gowdy J. Emergent architectures:a case study for outdoor mobile robots[D]. Robotics Institute, Carnegie Mellon University,2000.
[16]Thrun S, Montemerlo M, Dahlkamp H, et al. Stanley:the robot that won the DARPA grand challenge[J]. Journal of Field Robotics.2006,23(9):661-692.
[17]David S T, Ethan T T. Human-Robot Interaction. Carnegie Mellon Spring,2009.
[18]Nickel K, Stiefelhagen R. Real-time person tracking and pointing gesture recognition for human-robot interaction[J]. Lecture Notes in Computer Science.2004,3058:28-38.
[19]Song K T, Chen W J. Face recognition and tracking for human-robot interaction[C]. Proceedings of IEEE International Conference on Systems, Man and Cybernetics.2004, 3:2877-2882.
[20]Atienza R, Zelinsky A. Active gaze tracking for human-robot interaction[C]. Proceedings of the 4th IEEE International Conference on Multimodal Interfaces.2002: 261-266.
[21]Kanda T, Ishiguro H. Body movement analysis of human-robot interaction[C]. Proceedings of the 18th International Joint Conference on Artificial Intelligence. 2003:177-182.
[22]Cruz C, Sucar L E, Morales E F. Real-time face recognition for human-robot interaction[C]. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition.2008:1-6.
[23]Barreto J, Menezes P, Dias J. Human-robot interaction based on haar-like features and eigenfaces[C]. Proceedings of IEEE International Conference on Robotics and Automation.2004,2:1888-1893.
[24]Alper Y, Omar J, Mubarak S. Object tracking:A survey [J]. ACM Computing Surverys. 2006,38(4):1-45.
[25]侯志强,韩崇昭.视觉跟踪技术综述[J].自动化学报.2006,32(4)：603-617.
[26]Anderson C, Burt P, Van Der Wal G. Change detection and tracking using pyramid transform techniques [C]. Proceedings of SPIE-Intelligent Robots and Computer Vision. 1985:300-305.
[27]Chen L F, Liao H Y M, Lin J C. Wavelet-based optical flow estimation[J]. IEEE Transactions on Circuits and Systems for Video Technology.2002,12(1):1-12.
[28]Amiaz T, Kiryati N. Dense discontinuous optical flow via contour-based segmentation[C]. Proceedings of IEEE International Conference on Image Processing 2006,3:1264-1267.
[29]Mittal A, Paragios N. Motion-based background subtraction using adaptive kernel density estimation[C]. Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2004,2:302-309.
[30]Meyer F G, Bouthemy P. Region-based tracking using affine motion models in long image sequences[J]. Computer Vision and Image Understanding.1994,60(2):119-140.
[31]Bascle B, Deriche R. Region tracking through image sequences[C]. Proceedings of IEEE International Conference on Computer Vision.2002:302-307.
[32]Birchfield S T, Rangarajan S. Spatiograms versus histograms for region-based tracking[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition.2005:1158-1163
[33]Matthews L, Ishikawa T, Baker S. The template update problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(6):810-815.
[34]Avidan S. Support vector tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(8):1064-1072.
[35]Gang L, Yanghai T, Genc Y. Exploiting occluding contours for real-time 3D tracking:a unified approach[C]. Proceeding of IEEE 11th International Conference on Computer Vision.2007:1-8.
[36]Prisacariu V A, Timofte R, Zimmermann K, et al. Integrating object detection with 3D tracking towards a better driver assistance system[C]. Proceedings of 20th International Conference on Pattern Recognition.2010:3344-3347.
[37]邵文坤.便携式地面移动机器人目标跟踪方法研究[D].国防科学技术大学硕士学位论文,2005.
[38]安建虎.相关跟踪与特征匹配技术研究[D].中国科学院沈阳自动化研究所,2002.
[39]Michael Kass, Andrew Witkin D T. Snake:Active contour models[J]. International Journal of Computer Vision.1988,1(4):321-331.
[40]Menet S, Saint-Marc P, Mediom G. B-snakes:implementation and application to stereo[C]. Proceedings of the DARPA Image Understanding Workshop.1991:720-726.
[41]Kim W, Lee C Y, Lee J J. Tracking moving object using Snake's jump based on image flow[J]. Mechatronics.2001,11(2):199-226.
[42]Niethammer M, Tannenbaum A, Angenent S. Dynamic active contours for visual tracking[J]. IEEE Transactions on Automatic Control.2006,51(4):562-579.
[43]董春利,董育宁,刘杰.基于粒子滤波和GVF-Snake的目标跟踪算法[J].仪器仪表学报.2009,30(4)：828-833.
[44]Mitra P, Murthy C, Pal S K. Unsupervised feature selection using feature similarity [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2002,24(3):301-312.
[45]Kaneko T, Hori O. Feature selection for reliable tracking using template matching [C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2003:796-802.
[46]Kloihofer W, Kampel M. Interest point based tracking[C]. Proceedings of the 20th International Conference on Pattern Recognition.2010:3549-3552.
[47]Hofhauser A, Steger C, Navab N. Edge-Based template matching and tracking for perspectively distorted planar objects[M]. springer,2008.
[48]Zhang X, Yang J. The analysis of the color similarity problem in moving object detection[J]. Signal Processing.2009,89(4):685-691.
[49]Wang J, Yagi Yasushi. Integrating color and shape-texture words for adaptive real-time object tracking[J]. IEEE Transactions on Image Processing.2008,17(2):235-240.
[50]Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003:564-575.
[51]Gary R. Bradski. Computer vision face tracking for use in a perceptual user interface[J]. Intel Technology Journal Q2'98.1998:1-15.
[52]Wang Z, Yang X, Xu Y, et al. Camshift guided particle filter for visual tracking[J]. Pattern Recognition Letters.2009,30(4):407-413.
[53]Shaik Z, Asari V. A robust method for multiple face tracking using Kalman filter[C]. Proceedings of The 36th IEEE Applied Imagery Pattern Recognition Workshop.2008: 125-130.
[54]Kalkan S, Calow D, Wrgtter F, et al. Local image structures and optic flow estimation[J]. Network:Computation in Neural Systems.2005,16(4):341-356.
[55]Amiaz T, Kiryati N. Dense discontinuous optical flow via contour-based segmentation[C]. Proceedings of IEEE International Conference on Image Processing. 2006,3:1264-1267.
[56]查宇飞,张育,毕笃彦.基于区域活动轮廓运动目标跟踪方法研究[J].中国图象图形学报.2006,11(12)：1844-1848.
[57]Pang Y, Huang Q, Zhang W, et al. Real-time object tracking of a robot head based on multiple visual cues integration[C]. Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.2006:686-691.
[58]王宾,潘建寿,王琳等.基于多特征融合的均值偏移视频目标跟踪算法[J].小型微型计算机系统.2006,27(9)：1746-1749.
[59]孙凯.基于视觉的移动机器人运动目标跟踪研究[D].杭州电子科技大学,2009.
[60]Kung C, Kung C, Wang J. High performance real-time object detecting and tracking system for multiple moving targets[C]. Proceedings of IEEE Conference on Instrumentation and Measurement Technology.2007:1-4.
[61]Li T H S, Chang S J, Tong W. Fuzzy target tracking control of autonomous mobile robots by using infrared sensors[J]. IEEE Transactions on Fuzzy Systems.2004,12(4): 491-501.
[62]Guo X, Wang C, Qu Z. Object Tracking for Autonomous Mobile Robot based on Feedback of Monocular-Vision[C]. Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications.2007:467-470.
[63]Chen C H, Cheng C, Page D, et al. A moving object tracked by a mobile robot with real-time obstacles avoidance capacity[J]. Pattern Recognition.2006,3:1091-1094.
[64]梁冰,洪炳镕,曙光.基于视觉与行为模型的机器人目标跟踪[J].通信学报.2004,25(1)：92-99.
[65]Meng Y, Liu X, Liang Z. A robust method for mobile robot tracking object on behavior-based robotics[C]. Proceedings of the 1st IEEE International Symposium on Information Technologies and Applications in Education 2007:468-472.
[66]Kye Kyung K, Soo Hyun C, Hae Jin K, et al. Detecting and tracking moving object using an active camera[C]. Proceedings of the 7th International Conference on Advanced Communication Technology.2005,2:817-820.
[67]Asada M, Tanaka T, Hosoda K. Adaptive binocular visual servoing for independently moving target tracking[C]. Proceeding of IEEE International Conference on Roboties and Automation.2000,3:2076-2081.
[68]Weber J, Koller D, Luong Q T, et al. New results in stereo-based automatic vehicle guidance[C]. Proeeedings of the Intelligent Vechiles 95 Symposium.1995:530-535.
[69]Fang Y, Masaki I, Horn B. Depth-based target segmentation for intelligent vehicles: fusion of radar and binocular stereo [J]. IEEE Transactions on Intelligent Transportation Systems.2002,3(3):196-202.
[70]Tsalatsanis A, Valavanis K, Yalcin A. Vision based target tracking and collision avoidance for mobile robots[J]. Journal of Intelligent and Robotic Systems.2007,48(2): 285-304.
[71]Sanghoon K, Sangmu L, Seungjong K. Object tracking of mobile robot using moving color and shape information for the aged walking [C]. Proceedings of the 2nd International Conference on Future Generation Communication and Networking.2008, 2:293-297.
[72]高庆吉,洪炳熔,阮玉峰.基于异构双目视觉的全自主足球机器人导航[J].哈尔滨工业大学学报.2003,35(9)：1029-1032.
[73]祝琨.基于双目视觉信息的运动物体实时跟踪与测距[D].北京交通大学,2008.
[74]Buehler M, Iagnemma K, Singh S. The 2005 DARPA grand challenge:The great robot race[M]. Springer Verlag,2007.
[75]Urmson C, Anhalt J, Bagnell D, et al. Autonomous driving in urban environments:Boss and the urban challenge[J]. Journal of Field Robotics.2008,25(8):425-466.
[76]Betge-Brezetz S, Chatila R, Devy M. Natural scene understanding for mobile robot navigation[C]. Proceedings of IEEE International Conference on Robotics and Automation.1994,1:730-736.
[77]王璐,陆筱霞,蔡自兴.基于局部显著区域的自然场景识别[J].中国图象图形学报.2008,13(8)：1594-1600.
[78]张敏,刘利雄,贾云得.一种基于图像区域系综分类的室外场景理解方法[J].中国图象图形学报.2004,9(2)：1443-1448.
[79]Huhns M, Ruvinsky A, McCants D. The JIDOKA system for multiple-sensor terrain classification[R]. Technical Report CSE TR-2006-13, Center for Information Technology, University of South Carolina, USA,2006.
[80]Bosch A, Munoz X, Freixenet J. Segmentation and description of natural outdoor scenes[J]. Image and Vision Computing.2007,25(5):727-740.
[81]Posner I, Cummins M, Newman P. A generative framework for fast urban labeling using spatial and temporal context[J]. Autonomous Robots.2009,26(2):153-170.
[82]Li L J, Li F F. What, where and who? classifying event by scene and object recognition[C]. Proceedings of IEEE 11th International Conference on Computer Vision.2007:1-8.
[83]Li L J, Socher R, Li F F. Towards total scene understanding:Classification, annotation and segmentation in an automatic framework [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2009:2036-2043.
[84]Wang C, Blei D, Li F F. Simultaneous image classification and annotation[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2009: 1903-1910.
[85]许海霞,王耀南.一种分层Mean Shift目标跟踪算法[J].自动化学报.2009,35(4)：401-409.
[86]Zhong Y, Jain A K, Dubuisson-Jolly M P. Object tracking using deformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000, 22(5):544-549.
[87]Doucet A, Gordon Nei, Krishnamurthy. Particle filters for state estimation of jump Markov linear systems[J]. IEEE Transaction on Signal Processing.2001,49(3):613-624
[88]Jing L, Vadakkepat P. Interacting MCMC particle filter for tracking maneuvering target[J]. Digital Signal Processing.2010,20(2):561-574.
[89]Chung C Y, Chen H H. Video object extraction via MRF-based contour tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology.2010,20(1): 149-155.
[90]Wu M, Peng X, Zhang Q, et al. Patches-based Markov random field model for multiple object tracking under occlusion[J]. Signal Processing.2010,90(5):1518-1529.
[91]Maggio E, Piccardo E, Regazzoni C, et al. Particle PHD filtering for multi-target visual tracking [C]. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing.2007,1:1101-1104.
[92]Suga A, Fukuda K, Takiguchi T, et al. Object recognition and segmentation using SIFT and Graph Cuts[C]. Proceedings of the 19th International Conference on Pattern Recognition.2008:1-4.
[93]Lee D S. Effective gaussian mixture learning for video background subtraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(5):827-832.
[94]Nummiaro K, Koller-Meier E, Van Gool L. An adaptive color-based particle filter[J]. Image and Vision Computing.2003,21(1):99-110.
[95]Hue C L, Perez P. Tracking multiple objects with particle filtering[J]. IEEE Transactions on Aerospace and Electronic Systems.2002,38(3):791-812.
[96]Okuma K T, De Freitas N, Little J J, et al. A boosted particle filter:multitarget detection and tracking[J]. Lecture Notes in Computer Science.2004,3021:28-39.
[97]MacCormick J, F M. Stochastic algorithms for visual tracking[M]. Londen:Springer, 2002.
[98]Rudolph van der Merwe. The unscented particle filter[D]. Cambridge:Cambridge University.2000.
[99]Cheng H Y, Hwang J N. Adaptive particle sampling and adaptive appearance for multiple video object tracking [J]. Signal Processing.2009,89(9):1844-1849.
[100]方正,佟国峰,徐心和.粒子群优化粒子滤波方法[J].控制与决策.2007,22(3)：273-277.
[101]Zheng Y, Meng Y. Swarming particles with multi-feature model for free-selected object tracking[C]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.2008:2553-2558.
[102]Zhang X, Hu W, Maybank S, et al. Sequential particle swarm optimization for visual tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2008:1-8.
[103]Riklin Raviv T, Kiryati N, Sochen N. Prior-based segmentation by projective registration and level sets[C]. Proceedings of the10th IEEE International Conference on Computer Vision.2005,1:204-211.
[104]Michailovich O, Rathi Y, Tannenbaum A. Image segmentation using active contours driven by the bhattacharyya gradient flow[J]. IEEE Transactions on Image Processing. 2007,16(11):2787-2801.
[105]Isard M, Blake A. Condensation-conditional density propagation for visual tracking[J]. International Journal of Computer Vision.1998,29(1):5-28.
[106]Isard M, Blake A. Condensation:unifying low-level and high-level tracking in a stochastic framework[C]. Proceedings of the 5th European Conference on Computer Vision.1998:893-908.
[107]Rathi Y, Vaswani N, Tannenbaum A, et al. Tracking deforming objects using particle filtering for geometric active contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2007,29(8):1470-1475.
[108]李培华.主动轮廓线模型(蛇模型)综述[J].软件学报.2000,11(6)：751-757.
[109]Xu N, Ahuja N, Bansal R. Object segmentation using graph cuts based active contours[J]. Computer Vision and Image Understanding.2007,107(3):210-224.
[110]Mukherjee D P, Ray N, Acton S T. Level set analysis for leukocyte detection and tracking[J]. IEEE Transactions on Image Processing.2004,13(4):562-572.
[111]Xu C Y, Yezzi A Jr, Prince J L. On the relationship between parametric and geometric active contours[C]. Proceedings of the 43rd Asilomar Conference on Signals, Systems and Computers.2000,1:483-489
[112]张丽飞,王东峰.基于形变模型的图像分割技术综述[J].电子与信息学报2003,25(3)：395-403.
[113]Tannenbaum A, Angenent S. Dynamic Active Contours for Visual Tracking[J]. IEEE Transactions on Automatic Control.2006,51(4):562-579.
[114]Osher S, Sethian J. Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi formulations [J]. Journal of Computational Physics.1988, 79(1):12-49.
[115]Richard Tsai, Stanley O. Level set methods and their applications in image science[J]. Communications in Mathematical Sciences.2004,1(4):1-20.
[116]Adalsteinsson D, Sethian J A. A fast level set method for propagating interfaces[J]. Journal of Computational Physics.1995,118(2):269-277.
[117]Whitaker R T. A level-set approach to 3d reconstruction from range data[J]. International Journal of Computer Vision.1998,29(3):203-231.
[118]翁建广,庄越挺,潘云鹤.基于改进稀疏场算法的水平集形状过渡[J].软件学报.2006,17(7)：1544-1552.
[119]Comaniciu D, Meer P. Mean Shift:A robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24 (5):603-619.
[120]Comaniciu D. An algorithm for data-driven bandwidth selection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(2):281-288.
[121]Arulampalam M S, Maskell S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing. 2002,50(2):174-188.
[122]Jamasbi B, Motamedi S A, Behrad A. Tracking vehicle targets with large aspect change[C]. Proceedings of IEEE Workshop on Motion and Video Comouting.2007: 22-22.
[123]彭宁高,杨杰,刘志Mean-Shift跟踪算法中核函数窗宽的自动选取[J].软件学报.2004,16(9)：1542-1550.
[124]钱惠敏,茅耀斌,王执铨.自动选择跟踪窗尺度的Mean-Shift算法[J].中国图象图形学报.2007,12(2)：245-249.
[125]Li Z, Chen J, Schraudolph N N. An improved mean-shift tracker with kernel prediction and scale optimisation targeting for low-frame-rate video tracking [C]. Proceedings of the19th International Conference on Pattern Recognition.2008:1-4.
[126]Hu J S, Juan C W, Wang J J. A spatial-color mean-shift object tracking algorithm with scale and orientation estimation[J]. Pattern Recognition Letters.2008,29(16): 2165-2173.
[127]Liu H, Yu Z, Zha H, et al. Robust human tracking based on multi-cue integration and mean-shift[J]. Pattern Recognition Letters.2009,30(9):827-837.
[128]Kim J S, Hong K S. Color-texture segmentation using unsupervised graph cuts[J]. Pattern Recognition.2009,42(5):735-750.
[129]顾建栋,刘志,张兆杨.结合核密度估计和边缘信息的运动对象分割算法[J].计算机辅助设计与图形学学报.2009,21(2)：223-228.
[130]Lowe D G. Distinctive image words from scale-invariant keypoints[J]. International Journal of Computer Vision.2004,60(2):91-110.
[131]Zhou H, Yuan Y, Shi C. Object tracking using SIFT words and mean shift[J]. Computer Vision and Image Understanding.2009,113(3):345-352.
[132]厉茂海,洪炳镕,罗荣华.基于单目视觉的移动机器人全局定位[J].机器人.2007,29(2)：140-144.
[133]吕强,周文晖,刘济林.基于SIFT特征提取的单目视觉里程计在导航系统中的实现[J].传感技术学报.2007,20(5)：1148-1152.
[134]明安龙,马华东.多摄像机之间基于区域SIFT描述子的目标匹配[J].计算机学报.2008,31(4)：650-661.
[135]Yan K, Sukthankar R. PCA-SIFT:a more distinctive representation for local image descriptors[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2004,2:506-513.
[136]Mikolajczyk K, Schmid C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(10):1615-1630.
[137]Burghouts G J, Geusebroek J M. Performance evaluation of local colour invariants [J]. Computer Vision and Image Understanding.2009,113(1):48-62.
[138]Shan X, Wang Y. The matching method based on RANSAC algorithm for estimation of the fundamental matrix[J]. Journal of System Simulation.2006,12(17):2896-2900.
[139]Tuo H, Jing Z, Zhang T. Aerial sequence image mosaic using reduced sift descriptors[C]. Proceedings of International Conference on Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition.2007,6786: 1-9.
[140]Zhao T, Nevatia R. Tracking multiple humans in complex situations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(9):1208-1221.
[141]Yu T, Wu Y. Collaborative tracking of multiple targets[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2004,1:834-841.
[142]McKenna S J, Jabri S, Duric Z, et al. Tracking groups of people[J]. Computer Vision and Image Understanding.2000,80(1):42-56.
[143]Zhang L, Li Y, Nevatia R. Global data association for multi-object tracking using network flows[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2008:1-8.
[144]Fortmann T E, Barshalom Y, Scheffe M. Multi-Target tracking using joint probabilistic data association[C]. Proceedings of the 9th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.1980:807-812.
[145]Reid D. An algorithm for tracking multiple targets[J]. IEEE Transaction on Automat Control.1979,24(6):843-854.
[146]Karlsson R, Gustafsson F. Monte Carlo data association for multiple target tracking[C]. Proceedings of IEE International Seminar on Target Tracking:Algorithmsand Applications.2002,1:13-17.
[147]Schulz D, Burgard W, Fox D, et al. Tracking multiple moving targets with a mobile robot using particle filters and statistical data association[C]. Proceedings of IEEE International Conference on Robotics and Automation.2001,2:1665-1670.
[148]Sarkka S, Vehtari A, Lampinen J. Rao-Blackwellized Monte Carlo data association for multiple target tracking[C]. Proceedings of the 7th International Conference on Information Fusion.2004,7:583-590.
[149]Smith K, Gatica-Perez D, Odobez J M. Evaluating Multi-Object Tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2005, 36-43.
[150]梁敏,刘贵喜.基于自适应混合滤波的多目标跟踪算法[J].光学学报.2010,30(9)：2554-2561.
[151]刘国成,王永骥.一种基于改进粒子滤波的多目标跟踪算法.控制与决策.2009,24(2)：317-320
[152]Perera A, Srinivas C, Hoogs A. Multi-Object tracking through simultaneous long occlusions and split-merge conditions [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2006,1:666-673.
[153]Liu J S, Chen R. Sequential Monte Carlo methods for dynamic systems[J]. Journal of the American Statistical Association.1998,93(443):1032-1044.
[154]杨小军,潘泉,王睿.粒子滤波进展与展望[J].控制理论与应用.2006,23：261-267.
[155]Pitt M K, Shephard N. Filtering via simulation:Auxiliary particle filters[J]. Journal of the American Statistical Association.1999,94(2):590-599.
[156]Johnston C M, Mould N, Havlicek J P, et al. Dual domain auxiliary particle filter with integrated target signature update[C]. Proceedings of IEEE Conference on Computer Vision and Pattern.2009:54-59.
[157]Chen Z, Haykin S. On different facets of regularization theory [J]. Neural Computation. 2002,14(12):2791-2846.
[158]Antonacci F, Matteucci M, Migliore D, et al. Tracking multiple acoustic sources in reverberant environments using regularized particle filter[C]. Proceedings of International Conference on Digital Signal Processing.2007:99-102.
[159]Fox D. Adapting the sample size in particle filters through KLD-sampling[J]. The International Journal of Robotics Research.2003,22(12):985-1003.
[160]Liang Z, Ma X, Dai X. Robust tracking of moving sound source using scaled unscented particle filter[J]. Applied Acoustics.2008,69(8):673-680.
[161]Song C, Zhao H, Jing W, et al. A new particle swarm optimization based unscented particle filtering[C]. Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering.2009:1-4.
[162]Kotecha J H, Djuric P M. Gaussian particle filtering[J]. IEEE Transactions on Signal Processing.2003,51(10):2592-2601.
[163]Kotecha J H, Djuri P M. Blind sequential detection for Rayleigh fading channels using hybrid Monte Carlo-recursive identification algorithms[J]. Signal Processing.2004, 84(5):825-832.
[164]Bhaskar H, Mihaylova L, Maskell S. Population based particle filtering[C]. Proceedings of 2008 IET Seminar on Target Tracking and Data Fusion:Algorithms and Applications. 2008:31-38.
[165]张琪,王鑫,胡昌华.人工免疫粒子滤波算法的研究[J].控制与决策.2008,23(3)：293-301.
[166]Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics. 2002,26(1):29-41.
[167]吕强,刘士荣,邱雪娜.基于信息素机制的粒子群优化算法的设计与实现[J].自动化学报.2009,35(11)：1410-1419.
[168]方正,佟国峰,徐心和.基于粒子群优化的粒子滤波定位方法[J].控制理论与应用.2008,25(3)：533-534.
[169]Kenndy J, Eberhart R. Particle swarm optimization[C]. Proceedings of IEEE International Conference on Neural Networks.1995:1941-1948.
[170]Crisan D, Doucet A. A survey of convergence results on particle filtering methods for practitioners[J]. IEEE Transactions on Signal Processing.2002,50(3):736-746.
[171]Collins R T, Liu Y, Leordeanu M. Online selection of discriminative tracking words[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005:1631-1643
[172]Tang F, Brennan S, Zhao Q, et al. Co-tracking using semi-supervised support vector machines[C]. Proceedings of IEEE Conference on Computer Vision.2007:1-8.
[173]Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision.2008,77(1):125-141.
[174]Nguyen Dang B. A robust framework for visual object tracking[C]. Proceedings of International Conference on Computing and Communication Technologies.2009:1-8.
[175]席涛,张胜修,颜诗源.基于在线学习的自适应粒子滤波视频目标跟踪[J].光电工程.2010,37(6)：29-34.
[176]高琳,唐鹏,盛鹏.复杂场景下基于条件随机场的视觉目标跟踪[J].光学学报.2010,30(6)：1721-1728.
[177]温静.基于张量子空间学习的视觉跟踪方法研究[D].西安电子科技大学,2010.
[178]张辉,赵保军.基于概率主成分分析表观模型的视觉跟踪[J].系统工程与电子技术.2010,31(12)：2826-2829.
[179]Fukun B, Mingming B, Feng L, et al. A local descriptor based model with visual attention guidance for generic object detection[C]. Proceedings of the 3rd International Congress on Image and Signal Processing.2010,4:1599-1604.
[180]Tom Mitchell. Machine Learning. New York:McGraw Hill,1997.
[181]Tu Z. Probabilistic boosting-tree:Learning discriminative models for classification, recognition, and clustering[C]. Proceedings of IEEE Conference on Computer Vision. 2005,2:1589-1596.
[182]Serre T, Wolf L, Bileschi S, et al. Robust object recognition with cortex-like mechanisms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2007: 411-426.
[183]张骏,高隽,谢昭等.基于统计分析Boosting的复杂场景目标识别方法研究[J].仪器仪表学报.2010,31(8)：1788-1795.
[184]Ali S, Shah M. An integrated approach for generic object detection using kernel PCA and boosting[C]. Proceedings of IEEE International Conference on Multimedia and Expo.2005:1-4.
[185]Ali S, Shah M. A supervised learning framework for generic object detection in images [C]. Proceedings of the 10th IEEE International Conference on Computer Vision. 2005,2:1347-1354.
[186]Zaidi N A, Suter D. Confidence rated boosting algorithm for generic object detection[C]. Proceedings of the 19th International Conference on Pattern Recognition. 2008:1-4.
[187]Schapire R E. The strength of weak learnability[J]. Machine Learning.1990,5(2): 197-227.
[188]Freund Y. Boosting a weak learning algorithm by majority[J]. Information and Computation.1995,121(2):256-285.
[189]Schapire Y, Schapire F E. A decision-theoretic generalization of online learning and an application to boosting[J]. Journal of Computer and System Sciences.1997,55(1): 119-139.
[190]Freund Y, Schapire R E. Experiments with a new boosting algorithm[C]. Proceedings of the 13th International Conference on Machine Learning.1996:148-156.
[191]Schapire R E, Singer Y. Improved boosting algorithms using confidence-rated predictions[J]. Machine Learning.1999,37(3):297-336.
[192]Torralba A. A simple object detector with boosting[C]. Proceedings of ICCV short courses on Recognizing and Learning Object Categories.2005,1-5.
[193]Friedman J, Hastie T, Tibshirani R. Additive logistic regression:a statistical view of boosting[J]. The Annals of Statistics.2000,28(2):337-407.
[194]Csurka G, Dance C, Fan L, et al. Visual categorization with bags of keypoints[C]. Proceedings of ECCV Workshop on Statistical Learning for Computer Vision.2004,1: 59-74.
[195]Li F F, Pietro P. A bayesian hierarchical model for learning natural scene categories[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2005,2: 1-8.
[196]Sivic J, Russell B C, Efros A A, et al. Discovering object categories in image collections[C]. Proceedings of the IEEE International Conference on Computer Vision. 2005:1-8.
[197]Robert F, Li Feifei. Learning object categories from Google's image search[C]. Proceedings of IEEE International Conference on Computer Vision.2005,2: 1816-1823.
[198]Hofmann T. Unsupervised learning by probabilistic latent semantic analysis[J]. Machine learning.2001,42(1):177-196.
[199]Sivic J, Russell B C, Efros A A, et al. Discovering objects and their location in images [C]. Proceedings of the 10th International Conference on Computer Vision.2005, 1:370-377.
[200]Bryan Russell A T a W T. http://labelme.csail.mit.edu/,2009.
[201]邓汉成,王敏芳,王瑛.查全率与查准率之间关系的理论研究[J].情报学报.2000,19(4)：359—362
[202]Makhoul J, Kubala F, Schwartz R. Performance measures for information extraction[C]. Proceedings of DARPA Broadcast News Workshop.1999,249-252.
[203]Olson C F, Matthies L H. Maximum likelihood rover localization by matching range maps[C]. Proceedings of the IEEE International Conference on Robotics and Automation.2002,1:272-277.
[204]祝琨,杨唐文,阮秋琦等.基于双目视觉的运动物体实时跟踪与测距[J].机器人.2009,31(4)：327-334.
[205]Munoz Salinas R, Aguirre E, Garcia Silvente M, et al. A multiple object tracking approach that combines colour and depth information using a confidence measure [J]. Pattern Recognition Letters.2008,29(10):1504-1514.
[206]Faig W. Calibration of close-range photogrammetric system:mathmatical formulation [J]. Photogrammetric Engineering and Remote Sensing.1976,41(12): 1479-1486.
[207]Tsai R. An efficient and accurate camera calibration technique for 3D machine vision[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.1986:364-374.
[208]Tsai R. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE Journal of Robotics and Automation.1987,3(4):323-344.
[209]Weng J, Cohen P, Herniou M. Camera calibration with distortion models and accuracy evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2002, 14(10):965-980.
[210]Martins H, Birk J, Kelley R. Camera models based on data from two calibration planes[J]. Computer Graphics and Image Processing.1981,17(2):173-180.
[211]孟晓桥.摄像机自标定和一维重建中的若干问题研究[D].中国科学院,2000.
[212]崔彦平,林玉池.基于神经网络的双目视觉摄像机标定方法的研究[J].光电子.激光.2005,16(9)：1097-1100.
[213]傅其凤,崔彦平.双目视觉摄像机神经网络标定方法[J].工程图学学报.2005,6：93-97.