TXX无人机的自动跟踪算法研究
|
摘要
无人机较载人飞机而言具有很多优点,如制造成本低、重量较轻、体积较小、操作简单、使用方便、战场生存能力较强、隐身性能好、对作战环境要求低,并且可以准确、高效和灵便的执行各种军事或民用任务,因此备受世界各国青睐。然而,在无人机实际应用之前,必须首先对其进行靶场测试以确保无人机的性能稳定,但因其无人驾驶,需要在地而对其完成准确跟踪,才能方便地面人员了解无人机的状态。
本文设计了一套基于合作目标的无人机自动跟踪算法,该算法能够对图像进行预处理,对图像序列进行目标检测与目标识别,根据闽值分割后的图像提取目标有用信息,利用目标的运动模型完成目标轨迹的预测,并在预测位置附近搜索目标。主要做了以下工作：
分析和比较了无人机的跟踪方案,采用合作目标配合无人机的跟踪；通过对两种经典去噪方法进行对比分析,采用改进的中值滤波方法去除了图像中的噪声,对去噪后的灰度图像进行数学形态学处理,有效调整了图像中的明暗细节,使图像更加平滑；介绍了目标检测的方法,并根据分层投票表决法判断日标真伪,对含有真实目标的图像进行阈值分割。采用基于迭代的改进最大类间方差(Otsu)法,较完整地提取出了图像中的光斑,然后对光斑进行定位；利川Kalman滤波算法对目标轨迹进行预测,在预测位置附近,根据直方图信息进行K-S检验,判断当前搜索区域是否与目标模板匹配,并且给出了仿真结果；介绍了跟踪过程中所需要的坐标系及其之间的变换关系,并给出了激光器及CCD摄像机姿态的调整原则。
实验及仿真结果表明：基于选择排序法的中值滤波可以更加快速的去除图像中的噪声,改进的Otsu法可以解决图像分割时造成的误分割问题;识别目标后,Kalman滤波算法能够对目标轨迹进行有效预测,基于K-S检验的直方图匹配方法可以在预测位置附近找到最佳匹配位置,从而实现目标跟踪。这种目标跟踪算法能够满足目标跟踪的要求,不但可以有效识别目标,还能够提高目标跟踪的效率。
Compared with manned aircraft, UAV has many advantages, such as low cost, light weight, small volume, simplicity of operation, usableness, strong survivability, good stealth, low requirements for battle environment, and it can perform various military or civilian tasks accurately, efficiently and agilely. UAV is strongly favored by the countries all over the world. However, UAV must be range tested to ensure performance stable before actual application. Because of unmanned driving, UAV needs to be tracked accurately by researchers on the ground, which can help people to understand the state of the UAV.
TXX UAV automatic tracking algorithm based on cooperative object is designed in this thesis. What can be realized by this algorithm are image preprocessing, target detection and recognition of image sequence, extraction of target useful information on the basis of the image threshold segmented, prediction of target track by using moving model of the target, and target searching near the prediction location.
The tracking schemes of UAV are analyzed and compared, with the cooperative object being used to cooperate to track UAV. By the contrast and analysis of two classical de-noising methods, the improved median filter method is used to remove the noise off the image. Mathematical morphology processing is applied to adjust the brightness details to make de-noising image smoother. Target detection methods are introduced, and layering voting is used to identify the verity of target. The image containing true target is threshold segmented through improved Otsu method based on iteration. The facula can be extracted from the image completely and then be located. Target track is predicted by Kalman filtering algorithm. Near the prediction position, whether the current search area matches with the target template can be judged by using the K-S test based on histogram information, and simulation results are given. Finally, coordinate system and their transformation relations in tracking process are presented, and the attitude adjustment principle of lasers and CCD camera is given.
The experiment and simulation results indicated that the noise was removed off the image faster through median filter based on selection sort, and the problem that the target and background were segmented mistakenly was solved by improved Otsu method. After target recognition, the target track could be predicted effectively by using Kalman filtering algorithm. The optimal matched position was found near the prediction position through the histogram matching method based on K-S test. Finally, target tracking could be realized with this tracking algorithm. The requirement of target tracking was satisfied by this target tracking algorithm. The target could be recognized effectively, and also the efficiency of target tracking was improved.
本文设计了一套基于合作目标的无人机自动跟踪算法,该算法能够对图像进行预处理,对图像序列进行目标检测与目标识别,根据闽值分割后的图像提取目标有用信息,利用目标的运动模型完成目标轨迹的预测,并在预测位置附近搜索目标。主要做了以下工作：
分析和比较了无人机的跟踪方案,采用合作目标配合无人机的跟踪；通过对两种经典去噪方法进行对比分析,采用改进的中值滤波方法去除了图像中的噪声,对去噪后的灰度图像进行数学形态学处理,有效调整了图像中的明暗细节,使图像更加平滑；介绍了目标检测的方法,并根据分层投票表决法判断日标真伪,对含有真实目标的图像进行阈值分割。采用基于迭代的改进最大类间方差(Otsu)法,较完整地提取出了图像中的光斑,然后对光斑进行定位；利川Kalman滤波算法对目标轨迹进行预测,在预测位置附近,根据直方图信息进行K-S检验,判断当前搜索区域是否与目标模板匹配,并且给出了仿真结果；介绍了跟踪过程中所需要的坐标系及其之间的变换关系,并给出了激光器及CCD摄像机姿态的调整原则。
实验及仿真结果表明：基于选择排序法的中值滤波可以更加快速的去除图像中的噪声,改进的Otsu法可以解决图像分割时造成的误分割问题;识别目标后,Kalman滤波算法能够对目标轨迹进行有效预测,基于K-S检验的直方图匹配方法可以在预测位置附近找到最佳匹配位置,从而实现目标跟踪。这种目标跟踪算法能够满足目标跟踪的要求,不但可以有效识别目标,还能够提高目标跟踪的效率。
Compared with manned aircraft, UAV has many advantages, such as low cost, light weight, small volume, simplicity of operation, usableness, strong survivability, good stealth, low requirements for battle environment, and it can perform various military or civilian tasks accurately, efficiently and agilely. UAV is strongly favored by the countries all over the world. However, UAV must be range tested to ensure performance stable before actual application. Because of unmanned driving, UAV needs to be tracked accurately by researchers on the ground, which can help people to understand the state of the UAV.
TXX UAV automatic tracking algorithm based on cooperative object is designed in this thesis. What can be realized by this algorithm are image preprocessing, target detection and recognition of image sequence, extraction of target useful information on the basis of the image threshold segmented, prediction of target track by using moving model of the target, and target searching near the prediction location.
The tracking schemes of UAV are analyzed and compared, with the cooperative object being used to cooperate to track UAV. By the contrast and analysis of two classical de-noising methods, the improved median filter method is used to remove the noise off the image. Mathematical morphology processing is applied to adjust the brightness details to make de-noising image smoother. Target detection methods are introduced, and layering voting is used to identify the verity of target. The image containing true target is threshold segmented through improved Otsu method based on iteration. The facula can be extracted from the image completely and then be located. Target track is predicted by Kalman filtering algorithm. Near the prediction position, whether the current search area matches with the target template can be judged by using the K-S test based on histogram information, and simulation results are given. Finally, coordinate system and their transformation relations in tracking process are presented, and the attitude adjustment principle of lasers and CCD camera is given.
The experiment and simulation results indicated that the noise was removed off the image faster through median filter based on selection sort, and the problem that the target and background were segmented mistakenly was solved by improved Otsu method. After target recognition, the target track could be predicted effectively by using Kalman filtering algorithm. The optimal matched position was found near the prediction position through the histogram matching method based on K-S test. Finally, target tracking could be realized with this tracking algorithm. The requirement of target tracking was satisfied by this target tracking algorithm. The target could be recognized effectively, and also the efficiency of target tracking was improved.
引文
[1]李磊.无人机技术现状与发展趋势[J].硅谷.2011,(1)：46-47.
[2]张远民.基于机器视觉的无人机着降定位技术研究[D].成都：电子科技大学.2005.
[3]淳于江民,张珩.无人机的发展现状与展望[J].飞航导弹.2005,(2)：23-27.
[4]唐强,朱志强,王建元.国外无人机自主飞行控制研究[J].系统工程与电子技术.2004,(3)：418-422.
[5]Wren C R, Azarbayejani A, Darrell T, et al. Real-time tracking of the human body[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997,19(7):780-785.
[6]徐敏.基于目标运动信息的视频总结技术研究[D].合肥：中国科学技术大学.2009.
[7]王莉.复杂云彩背景下飞行小目标跟踪技术研究[D].沈阳：沈阳航空工业学院.2010.
[8]Avidan Shai. Support vector tracking[J]. IEEE Transactions Pattern Analysis and Machine Intelligence.2004,26(8):1064-1072.
[9]Meyer F, Bouthemy P. Region-based tracking using affine motion models in long image sequences[J]. CVCIP:Image understanding.1994,60(2):119-140.
[10]宋毅,崔平远,居鹤华.一种图像匹配中SSD和NCC算法的改进[J].计算机工程与应用.2006,02：42-44.
[11]Hu Weiming, Tan Tieniu, Wang Liang, et al. A survey on visual surveillance of object motion and behaviors[J]. IEEE Transaction System, Man, and Cybernetics-Part C: Applications and Reviews.2004,34(3):334-352.
[12]Jain A. K, Zhong Y. Lakshmanan. Object matching using deformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1996,18(3):267-278.
[13]Kass M., Witkin A., Terzopoulous D. Snakes:active contour models[C]. In Proceedings of the International Conference on Computer Vision. London:IEEE Computer Society,1987: 259-268.
[14]Haritaoglu I, Harwood D Davis L. W4:Real-time surveillance of People and their activities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000.22(8):809-830.
[15]Alatan A., Onural L., Wbllbom M.. Image Sequence Analysis for Emerging Interactive Multimedia Services-The European COST211 Brain work [J]. IEEE Transactions on Circuited System for Video Technology.1998.8(7):802-813.
[16]Robert T Collins. Alan J Lipton,Takeo Kanade. et al. A System for Video Surveillance and Monitoring[R]. Pittsburgh:Camegie Mellon University.2000:1-68.
[17]SiebelN.T., Mayband S. J. The ADVISOR Visual Surveillance System[J]. Proceedings of the ECCV 2004 workshop on Applications of Computer Vision.2004:103-111.
[18]Matsuyama T.. Cooperative Distributed Vision[J]. In Proceedings of DARPA Image Understanding Workshop.1998:365-384.
[19]李平安,敬忠良,胡士强.基于自适应表面模型的概率视频跟踪算法Ⅲ.控制‘j决策.2007,22(1)：53-58.
[20]田广,戚飞虎.移动摄像机环境下基于特征变换和SVM的分级行人检测算法[J].电子学报.2008,36(5)：1024-1028.
[21]Zhang Wei, Wu Q. M. J., Yang Xiaokang, et al. Multilevel Framework to Detect and Handle Vehicle Occlusion[J]. IEEE Transactions on Intelligent Transportation Systems. 2008,9(1):161-174.
[22]Li Yuan, Ai Haizhou, Yamashita T., et al. Tracking in Low Frame Rate Video:A Cascade Particle Filter with Discriminative Observers of Different Life spans[C]. IEEE Conference on Proceedings of Computer Vision and Pattern Recognition.2007,30(10):1-8.
[23]朱琳,周杰,宋靖燕.一种新的基于在线采样的多物体跟踪力法[J].计算机学报.2008,31(1)：151-160.
[24]焦波,李国辉.汪彦明.等.一种基于形态学的运动车辆阴影消除方法[J].自动化学报.2008,,34(7)：838-840.
[25]代科学,李国辉,涂丹等.监控视频运动目标监测减背景技术的研究现状和展望[J].中国图像图形报.2006,11(7)：919-927.
[26]李和平,胡古义,吴毅红,等基于半监督学习的行为建模与异常检测[J].软件、学报.2007,18(3)：527-537.
[27]彭晓明,丁明跃,川成平等.种利用信息融合的运动目标跟踪算法[J].计算机工程与应用.2003,30：28-30.
[28]杨晓峰,张桂林.一种基于仿射变换模型标跟踪算法[J].计算机与数字工程.2005,33(12)：30-34.
[29]牛新亮,赵国庆,刘原华,等.临近空间平台的超低空目标无源跟踪算法[J].西安电子科技大学学报：自然科学版.2009,36(3)：438-442.
[30]吕孝雷,张守宏.李锘.双基地向散射栅栏雷达的目标运动参数估计[J].西安电子科技大学学报：自然科学版.2007.34(1)：54-58.
[31]JensHacker. BerndHKroplin. An experimental study of visual flight trajectory tracking and pose prediction for the automatic computer control of a miniature airship[J]. Intelligent Computing Theory and Applications.2003:25-63.
[32]李清安.空中目标姿态测量技术及其仿真实验研究[D].长春：中国科学院长春光学精密机械与物理研究所,2006.
[33]徐晶,杨华民,方明,等.CCD激光经纬仪自动跟踪算法及软件实现方案[J].兵工学报,2008,29(6)：662-666.
[34]王习文,陈娟,王秋平.光电经纬仪跟踪飞行目标的一种仿真方法[J].计算机仿真,2009,(26)：38-42.
[35]黄琳.视频监控系统中的关键技术研究[D].成都：西南交通大学,2006.
[36]王津.视频监控系统中基于Kalman滤波器的目标跟踪技术研究[D].昆明：云南大学,2010.
[37]彭艳芳.视频运动目标检测与跟踪算法研究[D].武汉：武汉理工大学,2010.
[38]Tong Wuqin, Ling Yongshun, Yang Hua, et al. Multiple Motion Object Detection and Tracking Based on Hypothesis Theory in Sequence Image[J]. Opto-Electronic Engineering.2008,35(5):55-60.
[39]朱明旱,罗大庸.基于帧间差分背景模型的运动物体检测与跟踪[J].计算机测量与控制.2006,14(8)：1004-1006.
[40]Chen Changhong, Liang Jimin, Zhao Heng, et al. Frame difference energy image for gait recognition with incomplete silhouettes[J]. Pattern Recognition Letters.2009,30(11): 977-984.
[41]P.Spagnolo, T.D'Orazio, M. Leo, et al. Moving object segmentation by background subtraction and temporal analysis[J]. Image and Vision Computing.2006,24(5):411-423.
[42]Barron J L, Fleet D J, Beachemin S S. Performance of optical flow techniques[J]. International Journal of computer Vision.1994,12(1):42-47.
[43]杨枝灵,王开,等VISUAL C++数字图象获取处理及实践应用[M].人民邮电出版社,2003：457-462.
[44]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术.2002,24(16)：91-94.
[45]Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros. A novel multi-threshold segmentation approach based on differential evolution optimization[J]. Expert Systems with Applications. 2010.37(7):5265-5271.
[46]田小围,视频序列中运动目标跟踪算法研究[D].长春：长春理工大学,2008.
[47]何东健.数字图像处理[M].西安：现电子科技大学出版社,2008.
[48]丁金铸.基于图像处理的运动目标检测跟踪系统的设计与实现[D].武汉：华中师范大学2007.
[49]朱虹,潘英杰,冯春来,等.数字图像处理基础[M].北京：科学出版社,2005：84-93.
[50]卢胜伟.基于图像处理的目标识别跟踪研究[D].长春：长春理工大学,2008.
[51]杨卫平,沈振康.红外图像序列小目标检测预处理技术[J].红外与激光工程,1998.27(1)：23-28.
[52]王树文,闫成新,张天序,等.数学形态学在图像处理中的应用[J].计算机工程与应用,2004,(32)：89-92.
[53]张艳玲,刘桂雄,曹东,等.数学形态学的基本算法及在图像预处理中应用[J].科学技术与工程,2007,7(3)：356-359.
[54]Rafael C. Gonzalez, Richard E. Woods数字图像处理(2版)[M].阮秋琦,阮宇智,等,译.北京：电子工业出版社,2003：445-450.
[55]朱琴.多运动目标检测与跟踪算法的研究[D].南京：南京理工大学,2008.
[56]徐以美.视频序列中运动目标检测与跟踪技术研究[D].西安：西安电子科技大学,2008.
[57]徐萧萧,陈宗海.基于视觉信息的目标检测与跟踪技术现状与趋势[J].系统仿真技术及其应用.2007,9：932-937.
[58]王栓,艾海舟,何克忠.基于差分图象的多运动目标的检测与跟踪[J].中国像图形学报.1999,4(6)：470-475.
[59]杨卫平,李吉成,沈振康.分层投票表决目标检测方法及其性能分析[J].红外技术.2003,25(6)：10-13.
[60]许彬,郑链,王克勇,等.基于局域灰度概率分布的小目标检测方法[J].激光与红外.2005,,35(3)：187-189.
[61]刘艳红,李明,张永梅.两种计算机图像分割技术对比研究[J].山西农业大学学报：自然科学版.2008,28(2)：231-233.
[62]陈琪,熊博莅,陆军,等.改进的二维Otsu图像分割方法及其快速实现[J].电子与信息学报,2010,32(5)：1100-1104.
[63]曹亮.基于Otsu理论的图像分割算法研究[D].武汉：武汉理工大学.2008.
[64]杨修国.图像阈值分割方法研究与分析[D].上海：华东师范大学,2009.
[65]刘献如,蔡自兴.结构化道路车道线的鲁棒检测与跟踪[J].光电子·激光,2010.21(12)：1834-1838.
[66]Arora S, Acharya J, Verma A. et al. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm![J]. Pattern Recognition Letters.2008.29(11):119-125.
[67]夏正浩,徐贵力,程月华,等.基于视觉的无人机着舰中红外合作目标的分割方法研究[J].航空兵器.2009,(6)：28-30.
[68]Huang Dengyuan,Chia-Hung Wang. Optimal multi-level thresholding using a two stage Otsu optimization approach[J]. Pattern Recognition Letters.2009,30(11):275-284.
[69]余静,游志胜.自动目标识别与跟踪技术研究综述[J].计算机应用研究.2005,(1)：12-15.
[70]高明,吴振森.大气环境下激光光斑瞄准偏差的补偿方法研究[J].光学技术.2009,35(2)：252-255.
[71]李玉冰,郝永杰.刘恩海.多边形重心的计算方法[J].计算机应用.2005.25：391-393.
[72]王洪.基于粒子滤波的飞行目标视频跟踪系统研究[D].武汉:武汉理工大学.2010.
[73]Yang Yuanxi, Gao Weiguang. An Optimal Adaptive Kalman Filter[J]. Journal of Geodesy. 2006,80(4):177-183.
[74]田阗.一种多目标跟踪算法的研究与实现[D].西安：西安电子科技大学.2006.
[75]张波.基于粒子滤波的图像跟踪算法研究[D].上海：上海交通大学.2007.
[76]Tang Qi, Huang Jiangguo, Yang Xudong. A Novel Passive Tracking Algorithm Using Sensor-Array Based on Particle Filter[J]宇航学报.2007,28(2)：375-379.
[77]M.Fatih Talu, Servet Soyguder, omur Aydogmus. An implementation of a novel vision-based robotic tracking system[J]. Sensor Review.2010,30(3):225-232.
[78]Caifeng Shana, Tieniu Tan, Wei Yucheng. Real-time hand tracking using a mean shift embedded particle filter[J]. Pattern Recognition.2007,40(7):1958-1970.
[79]孙斌.图像序列中运动物体的检测与跟踪[D].天津：天津大学.2006.
[80]魏伟波.基于图像的目标自动识别与跟踪[D].南京：南京理工大学.2006.
[81]Zhai Haiting, Wu Xiaojuan, Han Hui. Research of a Real-time Hand Tracking Algorithm[C]. IEEE Internation Conference on Neural Networks and Brains.2005,2: 1233-1235.
[82]高恩克.目标跟踪的算法研究[D].西安：西安电子科技大学.2007.
[83]李鹏飞.基于MeanShift的目标跟踪算法研究[D].西安：西安电子科技大学.2009.
[84]王宇,程耀瑜.基于Kalman滤波原理的运动目标跟踪[J].2008,(10)：48-54.
[85]Lopes. RHC,Reid. I, Hobson. PR. The two-dimensional Kolmogorov-Smirnov test[J].Ⅺ International Workshop on Advanced Computing and Analysis Techniques in Physics Research.2007:23-27.
[86]赵选民,徐伟,师义民,等.数理统计[M].科学出版社,2002：171-177.
[87]宋志刚,何旭洪SPSS 16实用教程[M].人民邮电出版社,2008：274-276.
[88]刘惟锦,章毓晋.基于Kalman滤波和边缘直方图的实时目标跟踪[J].清华大学学报：自然科学版.2008,48(7)：1104-1107.
[89]李晶,范九伦.一种基于卡尔曼滤波的运动物体跟踪算法[J].计算机应用研究.2010,27(8)：3162-3164.
[90]张贝贝,肖国强,江健民.基于运动估计的Kalman滤波视频对象跟踪[J].计算机应用.2008,28(8)：2052-2054.
[91]E. Corvee. S. Velaslin. G. A. Jones. Occlusion Tolerent Tracking Using Hybrid Prediction Schemes[J]自动化学报.2003,29(3)：356-369.
[92]虞旦,韦巍,张远辉.一种基于卡尔曼预测的动态目标跟踪算法研究[J].光电工程.2009,36(1)：52-56.
[93]于起峰.陆宏伟.刘肖琳.基于图像的静密测量与运动测量[M].北京：科学出版社.2002：107-110.
[94]熊建平.边缘检测和摄像机标定算法的研究[D].南昌：南昌大学,2005.
[95]唐仁圣.空中目标实时跟踪算法研究及系统设计[D].重庆：重庆大学,2004.
[96]曲仕茹,史忠科.基于飞行器图像的目标跟踪方法研究[J].飞行力学.2001,19(4)：81-84.
[97]肖秦琨,雷斌.基于卡尔曼滤波的摄像头目标跟踪[J].西安工业学院学报,2006,26(1)：1-4.
[2]张远民.基于机器视觉的无人机着降定位技术研究[D].成都：电子科技大学.2005.
[3]淳于江民,张珩.无人机的发展现状与展望[J].飞航导弹.2005,(2)：23-27.
[4]唐强,朱志强,王建元.国外无人机自主飞行控制研究[J].系统工程与电子技术.2004,(3)：418-422.
[5]Wren C R, Azarbayejani A, Darrell T, et al. Real-time tracking of the human body[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997,19(7):780-785.
[6]徐敏.基于目标运动信息的视频总结技术研究[D].合肥：中国科学技术大学.2009.
[7]王莉.复杂云彩背景下飞行小目标跟踪技术研究[D].沈阳：沈阳航空工业学院.2010.
[8]Avidan Shai. Support vector tracking[J]. IEEE Transactions Pattern Analysis and Machine Intelligence.2004,26(8):1064-1072.
[9]Meyer F, Bouthemy P. Region-based tracking using affine motion models in long image sequences[J]. CVCIP:Image understanding.1994,60(2):119-140.
[10]宋毅,崔平远,居鹤华.一种图像匹配中SSD和NCC算法的改进[J].计算机工程与应用.2006,02：42-44.
[11]Hu Weiming, Tan Tieniu, Wang Liang, et al. A survey on visual surveillance of object motion and behaviors[J]. IEEE Transaction System, Man, and Cybernetics-Part C: Applications and Reviews.2004,34(3):334-352.
[12]Jain A. K, Zhong Y. Lakshmanan. Object matching using deformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1996,18(3):267-278.
[13]Kass M., Witkin A., Terzopoulous D. Snakes:active contour models[C]. In Proceedings of the International Conference on Computer Vision. London:IEEE Computer Society,1987: 259-268.
[14]Haritaoglu I, Harwood D Davis L. W4:Real-time surveillance of People and their activities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000.22(8):809-830.
[15]Alatan A., Onural L., Wbllbom M.. Image Sequence Analysis for Emerging Interactive Multimedia Services-The European COST211 Brain work [J]. IEEE Transactions on Circuited System for Video Technology.1998.8(7):802-813.
[16]Robert T Collins. Alan J Lipton,Takeo Kanade. et al. A System for Video Surveillance and Monitoring[R]. Pittsburgh:Camegie Mellon University.2000:1-68.
[17]SiebelN.T., Mayband S. J. The ADVISOR Visual Surveillance System[J]. Proceedings of the ECCV 2004 workshop on Applications of Computer Vision.2004:103-111.
[18]Matsuyama T.. Cooperative Distributed Vision[J]. In Proceedings of DARPA Image Understanding Workshop.1998:365-384.
[19]李平安,敬忠良,胡士强.基于自适应表面模型的概率视频跟踪算法Ⅲ.控制‘j决策.2007,22(1)：53-58.
[20]田广,戚飞虎.移动摄像机环境下基于特征变换和SVM的分级行人检测算法[J].电子学报.2008,36(5)：1024-1028.
[21]Zhang Wei, Wu Q. M. J., Yang Xiaokang, et al. Multilevel Framework to Detect and Handle Vehicle Occlusion[J]. IEEE Transactions on Intelligent Transportation Systems. 2008,9(1):161-174.
[22]Li Yuan, Ai Haizhou, Yamashita T., et al. Tracking in Low Frame Rate Video:A Cascade Particle Filter with Discriminative Observers of Different Life spans[C]. IEEE Conference on Proceedings of Computer Vision and Pattern Recognition.2007,30(10):1-8.
[23]朱琳,周杰,宋靖燕.一种新的基于在线采样的多物体跟踪力法[J].计算机学报.2008,31(1)：151-160.
[24]焦波,李国辉.汪彦明.等.一种基于形态学的运动车辆阴影消除方法[J].自动化学报.2008,,34(7)：838-840.
[25]代科学,李国辉,涂丹等.监控视频运动目标监测减背景技术的研究现状和展望[J].中国图像图形报.2006,11(7)：919-927.
[26]李和平,胡古义,吴毅红,等基于半监督学习的行为建模与异常检测[J].软件、学报.2007,18(3)：527-537.
[27]彭晓明,丁明跃,川成平等.种利用信息融合的运动目标跟踪算法[J].计算机工程与应用.2003,30：28-30.
[28]杨晓峰,张桂林.一种基于仿射变换模型标跟踪算法[J].计算机与数字工程.2005,33(12)：30-34.
[29]牛新亮,赵国庆,刘原华,等.临近空间平台的超低空目标无源跟踪算法[J].西安电子科技大学学报：自然科学版.2009,36(3)：438-442.
[30]吕孝雷,张守宏.李锘.双基地向散射栅栏雷达的目标运动参数估计[J].西安电子科技大学学报：自然科学版.2007.34(1)：54-58.
[31]JensHacker. BerndHKroplin. An experimental study of visual flight trajectory tracking and pose prediction for the automatic computer control of a miniature airship[J]. Intelligent Computing Theory and Applications.2003:25-63.
[32]李清安.空中目标姿态测量技术及其仿真实验研究[D].长春：中国科学院长春光学精密机械与物理研究所,2006.
[33]徐晶,杨华民,方明,等.CCD激光经纬仪自动跟踪算法及软件实现方案[J].兵工学报,2008,29(6)：662-666.
[34]王习文,陈娟,王秋平.光电经纬仪跟踪飞行目标的一种仿真方法[J].计算机仿真,2009,(26)：38-42.
[35]黄琳.视频监控系统中的关键技术研究[D].成都：西南交通大学,2006.
[36]王津.视频监控系统中基于Kalman滤波器的目标跟踪技术研究[D].昆明：云南大学,2010.
[37]彭艳芳.视频运动目标检测与跟踪算法研究[D].武汉：武汉理工大学,2010.
[38]Tong Wuqin, Ling Yongshun, Yang Hua, et al. Multiple Motion Object Detection and Tracking Based on Hypothesis Theory in Sequence Image[J]. Opto-Electronic Engineering.2008,35(5):55-60.
[39]朱明旱,罗大庸.基于帧间差分背景模型的运动物体检测与跟踪[J].计算机测量与控制.2006,14(8)：1004-1006.
[40]Chen Changhong, Liang Jimin, Zhao Heng, et al. Frame difference energy image for gait recognition with incomplete silhouettes[J]. Pattern Recognition Letters.2009,30(11): 977-984.
[41]P.Spagnolo, T.D'Orazio, M. Leo, et al. Moving object segmentation by background subtraction and temporal analysis[J]. Image and Vision Computing.2006,24(5):411-423.
[42]Barron J L, Fleet D J, Beachemin S S. Performance of optical flow techniques[J]. International Journal of computer Vision.1994,12(1):42-47.
[43]杨枝灵,王开,等VISUAL C++数字图象获取处理及实践应用[M].人民邮电出版社,2003：457-462.
[44]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术.2002,24(16)：91-94.
[45]Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros. A novel multi-threshold segmentation approach based on differential evolution optimization[J]. Expert Systems with Applications. 2010.37(7):5265-5271.
[46]田小围,视频序列中运动目标跟踪算法研究[D].长春：长春理工大学,2008.
[47]何东健.数字图像处理[M].西安：现电子科技大学出版社,2008.
[48]丁金铸.基于图像处理的运动目标检测跟踪系统的设计与实现[D].武汉：华中师范大学2007.
[49]朱虹,潘英杰,冯春来,等.数字图像处理基础[M].北京：科学出版社,2005：84-93.
[50]卢胜伟.基于图像处理的目标识别跟踪研究[D].长春：长春理工大学,2008.
[51]杨卫平,沈振康.红外图像序列小目标检测预处理技术[J].红外与激光工程,1998.27(1)：23-28.
[52]王树文,闫成新,张天序,等.数学形态学在图像处理中的应用[J].计算机工程与应用,2004,(32)：89-92.
[53]张艳玲,刘桂雄,曹东,等.数学形态学的基本算法及在图像预处理中应用[J].科学技术与工程,2007,7(3)：356-359.
[54]Rafael C. Gonzalez, Richard E. Woods数字图像处理(2版)[M].阮秋琦,阮宇智,等,译.北京：电子工业出版社,2003：445-450.
[55]朱琴.多运动目标检测与跟踪算法的研究[D].南京：南京理工大学,2008.
[56]徐以美.视频序列中运动目标检测与跟踪技术研究[D].西安：西安电子科技大学,2008.
[57]徐萧萧,陈宗海.基于视觉信息的目标检测与跟踪技术现状与趋势[J].系统仿真技术及其应用.2007,9：932-937.
[58]王栓,艾海舟,何克忠.基于差分图象的多运动目标的检测与跟踪[J].中国像图形学报.1999,4(6)：470-475.
[59]杨卫平,李吉成,沈振康.分层投票表决目标检测方法及其性能分析[J].红外技术.2003,25(6)：10-13.
[60]许彬,郑链,王克勇,等.基于局域灰度概率分布的小目标检测方法[J].激光与红外.2005,,35(3)：187-189.
[61]刘艳红,李明,张永梅.两种计算机图像分割技术对比研究[J].山西农业大学学报：自然科学版.2008,28(2)：231-233.
[62]陈琪,熊博莅,陆军,等.改进的二维Otsu图像分割方法及其快速实现[J].电子与信息学报,2010,32(5)：1100-1104.
[63]曹亮.基于Otsu理论的图像分割算法研究[D].武汉：武汉理工大学.2008.
[64]杨修国.图像阈值分割方法研究与分析[D].上海：华东师范大学,2009.
[65]刘献如,蔡自兴.结构化道路车道线的鲁棒检测与跟踪[J].光电子·激光,2010.21(12)：1834-1838.
[66]Arora S, Acharya J, Verma A. et al. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm![J]. Pattern Recognition Letters.2008.29(11):119-125.
[67]夏正浩,徐贵力,程月华,等.基于视觉的无人机着舰中红外合作目标的分割方法研究[J].航空兵器.2009,(6)：28-30.
[68]Huang Dengyuan,Chia-Hung Wang. Optimal multi-level thresholding using a two stage Otsu optimization approach[J]. Pattern Recognition Letters.2009,30(11):275-284.
[69]余静,游志胜.自动目标识别与跟踪技术研究综述[J].计算机应用研究.2005,(1)：12-15.
[70]高明,吴振森.大气环境下激光光斑瞄准偏差的补偿方法研究[J].光学技术.2009,35(2)：252-255.
[71]李玉冰,郝永杰.刘恩海.多边形重心的计算方法[J].计算机应用.2005.25：391-393.
[72]王洪.基于粒子滤波的飞行目标视频跟踪系统研究[D].武汉:武汉理工大学.2010.
[73]Yang Yuanxi, Gao Weiguang. An Optimal Adaptive Kalman Filter[J]. Journal of Geodesy. 2006,80(4):177-183.
[74]田阗.一种多目标跟踪算法的研究与实现[D].西安：西安电子科技大学.2006.
[75]张波.基于粒子滤波的图像跟踪算法研究[D].上海：上海交通大学.2007.
[76]Tang Qi, Huang Jiangguo, Yang Xudong. A Novel Passive Tracking Algorithm Using Sensor-Array Based on Particle Filter[J]宇航学报.2007,28(2)：375-379.
[77]M.Fatih Talu, Servet Soyguder, omur Aydogmus. An implementation of a novel vision-based robotic tracking system[J]. Sensor Review.2010,30(3):225-232.
[78]Caifeng Shana, Tieniu Tan, Wei Yucheng. Real-time hand tracking using a mean shift embedded particle filter[J]. Pattern Recognition.2007,40(7):1958-1970.
[79]孙斌.图像序列中运动物体的检测与跟踪[D].天津：天津大学.2006.
[80]魏伟波.基于图像的目标自动识别与跟踪[D].南京：南京理工大学.2006.
[81]Zhai Haiting, Wu Xiaojuan, Han Hui. Research of a Real-time Hand Tracking Algorithm[C]. IEEE Internation Conference on Neural Networks and Brains.2005,2: 1233-1235.
[82]高恩克.目标跟踪的算法研究[D].西安：西安电子科技大学.2007.
[83]李鹏飞.基于MeanShift的目标跟踪算法研究[D].西安：西安电子科技大学.2009.
[84]王宇,程耀瑜.基于Kalman滤波原理的运动目标跟踪[J].2008,(10)：48-54.
[85]Lopes. RHC,Reid. I, Hobson. PR. The two-dimensional Kolmogorov-Smirnov test[J].Ⅺ International Workshop on Advanced Computing and Analysis Techniques in Physics Research.2007:23-27.
[86]赵选民,徐伟,师义民,等.数理统计[M].科学出版社,2002：171-177.
[87]宋志刚,何旭洪SPSS 16实用教程[M].人民邮电出版社,2008：274-276.
[88]刘惟锦,章毓晋.基于Kalman滤波和边缘直方图的实时目标跟踪[J].清华大学学报：自然科学版.2008,48(7)：1104-1107.
[89]李晶,范九伦.一种基于卡尔曼滤波的运动物体跟踪算法[J].计算机应用研究.2010,27(8)：3162-3164.
[90]张贝贝,肖国强,江健民.基于运动估计的Kalman滤波视频对象跟踪[J].计算机应用.2008,28(8)：2052-2054.
[91]E. Corvee. S. Velaslin. G. A. Jones. Occlusion Tolerent Tracking Using Hybrid Prediction Schemes[J]自动化学报.2003,29(3)：356-369.
[92]虞旦,韦巍,张远辉.一种基于卡尔曼预测的动态目标跟踪算法研究[J].光电工程.2009,36(1)：52-56.
[93]于起峰.陆宏伟.刘肖琳.基于图像的静密测量与运动测量[M].北京：科学出版社.2002：107-110.
[94]熊建平.边缘检测和摄像机标定算法的研究[D].南昌：南昌大学,2005.
[95]唐仁圣.空中目标实时跟踪算法研究及系统设计[D].重庆：重庆大学,2004.
[96]曲仕茹,史忠科.基于飞行器图像的目标跟踪方法研究[J].飞行力学.2001,19(4)：81-84.
[97]肖秦琨,雷斌.基于卡尔曼滤波的摄像头目标跟踪[J].西安工业学院学报,2006,26(1)：1-4.