运动目标跟踪包括对视频序列中的运动目标进行实时监测、分析、提取、跟踪以及行为描述等过程，是一门综合性技术，融合了图像处理、模式识别、人工智能等众多学科技术，在民用和军事应用中均有着广泛的用途。本文在运动目标跟踪中的主要理论方法的基础上进行总结，针对其不足进行适当改善以提高算法的跟踪性能。 特征的提取在基于图像的目标跟踪技术研究中发挥着重要的作用，由于角点特征算子在特征匹配中表现出了良好的鲁棒性，而且广泛应用于目标跟踪的meanshift算法在非线性、非高斯系统的状态估计中具有优越性，因此将meanshift与角点特征相结合用于复杂场景的目标跟踪具有重要的研究价值。本文介绍了两种典型的角点特征...Moving target tracking include video sequence of moving targets real-time monitoring, analysis, extraction, tracking, and behavioral description of the process,is a comprehensive technology integration of image processing, pattern recognition, artificial intelligence, many disciplines in the civilwide range of applications and military applications. On the basis of the moving target tracking in th...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332008115327

陈侠

Xiamen University Institutional Repository

 学校编码：10384                                 分类号      密级      学号：23320081153275                                         UDC_____   硕 士 学 位 论 文  基于改进的 Meanshift 跟踪算法研究 The research for an improved Meanshift tracking algorithm 陈 侠 指导教师姓名：陈辉煌 教授 专业名称：通信与信息系统 论文提交日期：2012 年   月 论文答辩时间：2012 年   月 学位授予日期：2012 年   月                                            答辩委员会主席：_________                          评    阅    人：___________  2012 年  月厦门大学博硕士论文摘要库厦门大学博硕士论文摘要库  厦门大学学位论文原创性声明  本人呈交的学位论文是本人在导师指导下，独立完成的研究成果。本人在论文写作中参考其他个人或集体已经发表的研究成果，均在文中以适当方式明确标明，并符合法律规范和《厦门大学研究生学术活动规范(试行)》。 另外，该学位论文为(             )课题(组)的研究成果，获得(            )课题(组)经费或实验室的资助，在(          )实验室完成。(请在以上括号内填写课题或课题组负责人或实验室名称，未有此项声明内容的，可以不作特别声明。)  声明人(签名)：           年   月   日        厦门大学博硕士论文摘要库厦门大学博硕士论文摘要库 厦门大学学位论文著作权使用声明  本人同意厦门大学根据《中华人民共和国学位条例暂行实施办法》等规定保留和使用此学位论文，并向主管部门或其指定机构送交学位论文(包括纸质版和电子版)，允许学位论文进入厦门大学图书馆及其数据库被查阅、借阅。本人同意厦门大学将学位论文加入全国博士、硕士学位论文共建单位数据库进行检索，将学位论文的标题和摘要汇编出版，采用影印、缩印或者其它方式合理复制学位论文。 本学位论文属于： (     )1.经厦门大学保密委员会审查核定的保密学位论文，于  年  月  日解密，解密后适用上述授权。 (  √ )2.不保密，适用上述授权。 (请在以上相应括号内打“√”或填上相应内容。保密学位论文应是已经厦门大学保密委员会审定过的学位论文，未经厦门大学保密委员会审定的学位论文均为公开学位论文。此声明栏不填写的，默认为公开学位论文，均适用上述授权。)                                声明人(签名)： 年   月   日  厦门大学博硕士论文摘要库厦门大学博硕士论文摘要库 摘 要 运动目标跟踪包括对视频序列中的运动目标进行实时监测、分析、提取、跟踪以及行为描述等过程，是一门综合性技术，融合了图像处理、模式识别、人工智能等众多学科技术，在民用和军事应用中均有着广泛的用途。本文在运动目标跟踪中的主要理论方法的基础上进行总结，针对其不足进行适当改善以提高算法的跟踪性能。 特征的提取在基于图像的目标跟踪技术研究中发挥着重要的作用，由于角点特征算子在特征匹配中表现出了良好的鲁棒性，而且广泛应用于目标跟踪的meanshift 算法在非线性、非高斯系统的状态估计中具有优越性，因此将 meanshift与角点特征相结合用于复杂场景的目标跟踪具有重要的研究价值。本文介绍了两种典型的角点特征算子的原理并结合 meanshift，提出一种 meanshift 框架下利用角点特征作为第二特征集修正目标位置的跟踪算法，并结合 kalman 滤波器、有效的模板选择和更新策略，提高了算法的跟踪精度以及在复杂场景下的自适应能力。 在上述研究的基础上，论文针对 meanshift 在跟踪目标发生大比例缩放的情况下由于缺少模板更新策略存在的不足，提出在建立模板之初加入插值算法，对选定模板进行缩放，建立候选目标模板，并在跟踪过程中选用跟踪效果较好的候选模板为目标模板，以此来削弱目标发生大比例缩放而产生的误差。 论文的 后对改进算法的运行效果进行分析，总结论文的创新点，并指出进一步的工作。 关键词：Mean-shift，角点特征；自适应滤波；插值算法  厦门大学博硕士论文摘要库厦门大学博硕士论文摘要库 Abstract Moving target tracking include video sequence of moving targets real-time monitoring, analysis, extraction, tracking, and behavioral description of the process,is a comprehensive technology integration of image processing, pattern recognition, artificial intelligence, many disciplines in the civilwide range of applications and military applications. On the basis of the moving target tracking in the major theoretical approaches to sum up for its shortage of appropriate improvements in order to improve the tracking performance of the algorithm Feature extraction in image-based tracking technology to play an important role ,as corner features in the feature match operator shows a good robustness,and is widely used in target tracking algorithm in nonlinear meanshift Non-Gaussian state estimation system has advantages,it will meanshift combined with the corner feature tracking for complex scenes of great reseach value.This article describes two typical characteristics of the corner of the principle operator and combining meanshift,the framework proposes a meanshift corner feature using feature set as the second amendment to the target location of the tracking algorithm,combines with efficient template selection and update policy improve the tracking accuracy and the ability of complex adaptive scenaios.  The paper focused on analyzing the advantages and disadvantages of the classic meanshift algorithm, and track the target for a major meanshift in the case of scaling the template update policy due to lack of shortcomings, proposed the establishment of the beginning of the template by adding interpolation algorithm for scaling the selected template establishment of candidate target template, and the process used in tracking to track the better candidate template for the target template in order to undermine the goal of a major scaling arising from the error. Finally, the paper's operating results improved algorithm to analyze, summarize the innovation thesis and pointed out that further work. Key Words: Mean-shift; Corner Feature; particle filter;Interpolation algorithm 厦门大学博硕士论文摘要库厦门大学博硕士论文摘要库 目 录 摘 要 ........................................................................................................... 1 Abstract ...................................................................................................... 1 第一章 绪论 .............................................................................................. 1 1.1 选题背景及意义 .......................................................................................... 1 1.2 运动目标跟踪基本内容及现状 .................................................................. 2 1.2.1 国内外研究现状 .................................................................................. 2 1.2.2 研究主要内容 ...................................................................................... 3 1.3 运动目标跟踪的基本方法 .......................................................................... 4 1.4 论文的结构安排 .......................................................................................... 7 第二章 基于特征的目标跟踪算法研究 .................................................. 9 2.1 基于特征的目标跟踪技术 .......................................................................... 9 2.1.1 目标特征的选择 .................................................................................. 9 2.1.2 几种常见的目标特征 ........................................................................ 10 2.1.3 基于特征的跟踪算法结构 ................................................................ 11 2.2 常用的特征提取算法 ................................................................................ 12 2.2.1 角点特征提取算法 ............................................................................ 12 2.2.2 实验结果与分析 ................................................................................ 15 2.2.3 边缘特征提取算法 ............................................................................ 17 2.2.4 尺度不变特征提取算法 .................................................................... 18 2.3 本章小结 .................................................................................................... 19 第三章 自适应滤波概述 ........................................................................ 21 3.1 自适应滤波理论 ........................................................................................ 21 3.2 自适应滤波器的基本原理 ........................................................................ 21 3.2.1 LMS 自适应滤波理论 ...................................................................... 23 3.2.2 贝叶斯滤波理论[40] ........................................................................... 24 3.2.3 卡尔曼滤波理论[41] [42] ...................................................................... 26 厦门大学博硕士论文摘要库 3.2.4 扩展卡尔曼滤波理论 ........................................................................ 28 3.2.5 粒子滤波理论 .................................................................................... 31 3.3 本章小结 .................................................................................................... 36 第四章 自适应滤波框架下的 meanshift 跟踪算法研究 ..................... 37 4.1 Meanshift 基本理论[45] ............................................................................. 37 4.1.1 多维空间下的无参密度估计 ............................................................ 37 4.1.2 Mean-shift 向量 ................................................................................. 38 4.2 Meanshift 在目标跟踪中的应用 .............................................................. 39 4.2.1 起始目标模型与候选目标模型 ........................................................ 39 4.2.2 相似性函数与目标定位 .................................................................... 40 4.2.3 实验结果与分析 ................................................................................ 41 4.3 粒子滤波框架下的 meanshift 跟踪算法 ................................................. 46 4.3.1 粒子滤波框架下的 meanshift 跟踪算法 ......................................... 46 4.3.2 实验结果与分析 ................................................................................ 47 4.4 基于卡尔曼滤波器的 meanshift 跟踪算法 ............................................. 49 4.4.1 卡尔曼滤波器建模 ............................................................................ 49 4.4.2 算法流程图 ........................................................................................ 50 4.4.3 实验结果与分析 ................................................................................ 51 4.5 本章小结 .................................................................................................... 54 第五章 基于改进的 Meanshift 跟踪算法研究 ..................................... 55 5.1 引言 ............................................................................................................ 55 5.2 基于改进的 mean-shift 跟踪算法 ............................................................ 55 5.2.1 算法基本思想 .................................................................................... 55 5.2.2 基于强角点的 Meanshift 跟踪算法基本过程 ................................. 57 5.2.3 目标位置修正 .................................................................................... 58 5.2.4 实验结果与分析 ................................................................................ 58 5.3 模板的选择 ................................................................................................ 63 5.4 本章小结 .................................................................................................... 66 第六章 总结与展望 ................................................................................ 67 厦门大学博硕士论文摘要库 6.1 工作总结 .................................................................................................... 67 6.2 研究展望 .................................................................................................... 68 参 考 文 献 ............................................................................................ 69 攻读硕士学位期间发表的论文 .............................................................. 73 致 谢 ......................................................................................................... 75 厦门大学博硕士论文摘要库厦门大学博硕士论文摘要库 Contents Abstract ...................................................................................................... 1 Abstract ...................................................................................................... 1 Chapter 1 Introduction ............................................................................. 1 1.1 Research background and significance .................................................... 1 1.2 Tracking the basic content and status ...................................................... 2 1.2.1 Research Status .................................................................................... 2 1.2.2 Technical Difficulties ........................................................................... 3 1.3 Summary of Monving Object tracking Algorithm .................................. 4 1.4 The Rearch and Arrangement of Thesis ................................................... 7 Chapter 2 Research for Feature Matching Tracking Algorithm .......... 9 2.1 Technology of Feature-based Object tracking ......................................... 9 2.1.1 The choice of target features ................................................................ 9 2.1.2 Characteristics of several common goals ........................................... 10 2.1.3 Feature-based tracking algorithm structure ................................................ 11 2.2 Commonly used feature extraction algorithm ....................................... 12 2.2.1 Feature Point Extraction Algorithm ................................................... 12 2.2.2 Experimental results and analysis ...................................................... 15 2.2.3 Edge feature extraction algorithm ...................................................... 17 2.2.4 Scale-invariant feature extraction algorithm ...................................... 18 2.3 Summary ................................................................................................... 19  Chapter 3 Overview of Adaptive Filtering ......................................... 21 3.1 Adaptive Filter Theory ............................................................................. 21 3.2 The basic principle of adaptive filter ...................................................... 21 3.2.1 LMS Adaptive Filter Theory .............................................................. 23 3.2.2 Bayesian filtering theory .................................................................... 24 3.2.3 Kalman Filter Theory [38][39] .................................................................... 26 3.2.4 Extended Kalman Filter Theory ......................................................... 28 厦门大学博硕士论文摘要库 3.2.5 Particle Filter Theory ......................................................................... 31 3.3 Summary ................................................................................................... 36  Chapter 4 Under the framework of adaptive filtering algorithm for mean shift tracking ................................................................................. 37 4.1 The basic theory of Mean Shift [51] .......................................................... 37 4.1.1 Multidimensional density estimation under the non-participation .... 37 4.1.2 Mean-shift vector ............................................................................... 38 4.2 Mean Shift in Target Tracking ................................................................ 39 4.2.1 Initial target model and candidate target model .......................................... 39 4.2.2 Similarity function and the target location ................................................. 40 4.3 Under the framework of particle filter tracking algorithm mean-shift ......................................................................................................... 46 4.3.1 Particle filter based on mean shift tracking algorithm .................................. 46 4.3.2 Experimental results and analysis ............................................................. 47 4.4 Kalman filter based on mean shift tracking algorithm ................ 49 4.4.1 Kalman filter model ............................................................................... 49 4.4.2 Algorithm flow chart .............................................................................. 50 4.4.3 Experimental results and analysis ...................................................... 51 4.5 Summary ................................................................................................... 54  Chapter 5 Corner Feature-based tracking algorithm ....................... 55 5.1 Introduction .............................................................................................. 55 5.2 Corner features based on mean-shift tracking algorithm .......... 55 5.2.1 Basic idea of algorithm ...................................................................... 55 5.2.2 Track the target mean shift algorithm flow chart ............................... 57 5.2.3 Target position correction .................................................................. 58 5.2.4 Experimental results and analysis ...................................................... 58 5.3 Template selection and update .......................................................... 63 5.4 Summary ................................................................................................... 66 厦门大学博硕士论文摘要库Degree papers are in the “Xiamen University Electronic Theses and Dissertations Database”. 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The reseaech for an improved meanshift tracking algorithm

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The reseaech for an improved meanshift tracking algorithm

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