运动目标检测及其行为分析研究

Contents

摘要 ....................................................................................................................... I ABSTRACT ............................................................................................................ I II 第1章绪论 (1)

1.1课题背景 (1)

1.2研究的目的和意义 (2)

1.3视频分析方法概述 (3)

1.3.1 运动目标检测方法 (4)

1.3.2 动作识别方法 (7)

1.3.3 异常行为分析方法 (9)

1.3.4 人数统计及其行为分析 (11)

1.4本课题建立的数据集 (14)

1.5本文的主要研究内容及结构安排 (15)

第2章联合位置分布与低秩分解的运动目标检测方法 (18)

2.1引言 (18)

2.2问题描述 (20)

2.3目标位置信息估计 (23)

2.4有下界的类间距离最大划分 (24)

2.4.1 帧集划分模型 (24)

2.4.2 模型求解 (27)

2.5联合有下界的类间距离最大化与低秩分解的运动目标检测 (31)

2.5.1 增广集构建 (31)

2.5.2 算法描述 (32)

2.5.3 算法参数设置 (33)

2.6实验分析 (34)

2.6.1 人工点集测试 (35)

2.6.2 在视频数据集上的实验 (37)

2.6.3 算法复杂度分析 (44)

2.7本章小结 (46)

第3章一种无监督最优特征选择的动作识别方法 (47)

3.1引言 (47)

3.2相关研究工作 (49)

3.2.1 Bag-of-words特征表示 (49)

3.2.2 基于子空间方法的动作识别 (50)

3.2.3 主成分分析方法 (50)

3.2.4 局部近邻保持嵌入方法 (51)

3.2.5 稀疏保持投影方法 (51)

3.2.6 基于旋转不变的回归分类方法 (52)

3.2.7 竞争样本选择方法 (52)

3.3动作识别框架 (53)

3.4最优特征选择模型 (54)

3.5模型求解 (57)

3.5.1 固定稀疏表示矩阵计算投影矩阵 (57)

3.5.2 固定投影矩阵计算稀疏表示矩阵 (59)

3.5.3 估计稀疏表示矩阵的初值 (60)

3.6基于最优特征选择的分类方法 (61)

3.7模型分析 (62)

3.7.1 算法收敛性证明 (62)

3.7.2 算法复杂度分析 (66)

3.7.3 关于最优特征选择方法的一个特例 (67)

3.8实验分析 (67)

3.8.1 参数设置与寻优策略 (68)

3.8.2 常规人体行为数据集上的实验 (69)

3.8.3 复杂行为动作数据集上的实验 (71)

3.8.4 模型变量实验性分析 (73)

3.8.5 关于算法收敛的实验分析 (78)

3.8.6 识别时间对比 (79)

3.9本章小结 (79)

第4章基于多方向高斯模型与权重随机抽样的异常运动检测方法 (81)

4.1引言 (81)

4.2相关研究工作 (83)

4.2.1 异常速度检测 (83)

4.2.2 随机抽样一致性算法 (85)

4.3异常运动检测系统框架 (87)

4.4关于运动速度与视角变化的理论分析 (89)

Contents

4.5基于多方向高斯模型速度统计与学习 (97)

4.5.1 基于改进Fisher准则确定最优方向区间 (100)

4.5.2 多方向区间的运动速度统计与学习 (105)

4.6基于权重随机抽样一致性算法的速度场曲面拟合 (107)

4.6.1 基于K均值聚类的区域划分 (109)

4.6.2 服从递减概率分布的采样方式 (110)

4.6.3 速度在多方向区间的三维曲面拟合 (112)

4.7异常行为概率判断模型 (114)

4.8实验分析 (115)

4.8.1 方向区间确定实验 (115)

4.8.2 速度场曲面拟合实验 (121)

4.8.3 快速移动检测实验 (127)

4.9本章小结 (138)

第5章结合ADABOOST与多特征的人数统计 (139)

5.1引言 (139)

5.2相关研究工作 (139)

5.2.1 AdaBoost检测算法 (140)

5.2.2 MeanShift跟踪算法 (141)

5.3人数统计算法 (142)

5.4垂直视角下的目标检测 (144)

5.5多特征提取 (146)

5.5.1 目标匹配响应特征 (146)

5.5.2 目标运动强度特征 (148)

5.5.3 目标尺度特征 (149)

5.6目标跟踪和人数统计方案 (150)

5.6.1 最近邻跟踪与计数准则 (150)

5.6.2 基于最优颜色聚类的MeanShift跟踪与计数准则 (151)

5.7人数统计系统搭建 (154)

5.8实验分析 (155)

5.8.1 系统去误检干扰和对多目标的处理结果 (156)

5.8.2 在非繁忙时段视频的实验 (161)

5.8.3 在繁忙时段视频的实验 (161)

5.8.4 携带货物视频的实验 (162)

5.8.5 光线变化情况下的实验 (162)

5.8.6 最优颜色聚类实验结果 (164)

5.8.7 各种方法在视频数据库上的测试结果对比 (168)

5.8.8 人数统计视频中的行为分析 (169)

5.9本章小结 (170)

结论 (171)

参考文献 (174)

攻读博士学位期间发表的论文及其他成果 (191)

哈尔滨工业大学学位论文原创性声明和使用权限 (193)

致谢 (194)

个人简历 (196)

Contents

Abstract (In Chinese)........................................................................................?Abstract (In English)............................................................................................III Chapter 1 Introduction (1)

1.1 Background of the thesis (1)

1.2 Research objective and significance (2)

1.3 Introduction of the methods in video analysis (3)

1.3.1 Methods of moving object detection (4)

1.3.2 Methods of action recognition (7)

1.3.3 Methods of abnormal action analysis (9)

1.3.4 Methods of people counting and activity analysis (11)

1.4 Introduction of the datasets built in this work (14)

1.5 Main research contents and structure organization of this thesis (15)

Chapter 2 Joint position distribution and low-rank decomposition for background subtraction (18)

2.1 Introduction (18)

2.2 Problem description (20)

2.3 Estimation of target position (23)

2.4 Lower bound-based within-class maximum division (24)

2.4.1 Frame set division model (24)

2.4.2 Solution of model (27)

2.5 Lower bound-based within-class maximum and low-rank decomposition for background subtraction (31)

2.5.1 Augmented set construction (31)

2.5.2 Algorithm description (32)

2.5.3 Parameter setting (33)

2.6 Experimental analysis (34)

2.6.1 Testing on artificial point set (35)

2.6.2 Experiments on video datasts (37)

2.6.3 Complexity analysis of the proposed algorithm (44)

2.7 Summary of this chapter (46)

Chapter 3 An unsupervised optimal feature selection method for action recognition (47)

3.1 Introduction (47)

3.2 Related work (49)

3.2.1 Representation of bag-of-words feature (49)

3.2.2 Subspace-based methods for action recognition (50)

3.2.3 Principle component analysis (50)

3.2.4 Neighborhood preserving embedding (51)

3.2.5 Sparsity preserving projection (51)

3.2.6 Rotational invariant norm based regression for classification (52)

3.2.7 Competitive sample selection method (52)

3.3 Framework of action recognition (53)

3.4 Optimal feature selection model (54)

3.5 Solution of the model (57)

3.5.1 Fix sparse representation matrix to solve projection matrix (57)

3.5.2 Fix projection matrix to solve sparse representation matrix (59)

3.5.3 Estimation of the initial value of sparse representation matrix (60)

3.6 Classification based on UOFS (61)

3.7 Model analysis (62)

3.7.1 Convergence proof of the algorithm (62)

3.7.2 Complexity analysis of the algorithm (66)

3.7.3 A special case of UOFS (67)

3.8 Experimental analysis (67)

3.8.1 Parameter setting and searching stargegy (68)

3.8.2 Experiments on hand gesture action datasets (69)

3.8.3 Experiments on human action datasets (71)

3.8.4 Experimental analysis of the variables (73)

3.8.5 Experimental analysis on the convergence of the proposed algorithm (78)

3.8.6 Comparison of recognition time (79)

3.9 Summary of this chapter (79)

Chapter 4 Combination of multiple directional Gaussian model and weighted random sample for anomaly detection (81)

4.1 Introduction (81)

4.2Introduction of related work (83)

4.2.1 Fast moving detection (83)

4.2.2 Random sample consensus algorithm (85)

4.3 System architecture of fast moving detection (87)

4.4Theory analysis in terms of the relation between moving velocity and view changes (89)

4.5 Statistic and learning of velocity based on multiple directional Gaussian model (97)

4.5.1 Determination of optimal direction bin based on improved Fisher

principle (100)

4.5.2 Statistic and learning of motion velocity based on multiple direction

bins (105)

Contents

4.6Curve fitting for velocity field based on weighted random sample consensus (107)

4.6.1 Region division based on K-means clustering (109)

4.6.2 Sampling method following decreesed probability distribution (110)

4.6.3 Curve fitting for velocity fields in multiple direction bins (112)

4.7 Probabilistic decision model for fast moving detection (114)

4.8 Experimental analysis (115)

4.8.1 Experiments on the determination of direction bins (115)

4.8.2 Experiments on curve fitting for velocity fields (121)

4.8.3 Experiments on fast moving detection (127)

4.9 Summary of this chapter (138)

Chapter 5 Combination of ADABOOST and multiple features for people counting (139)

5.1 Introduction (139)

5.2 Related work (139)

5.2.1 AdaBoost detection algorithm (140)

5.2.2 MeanShift tracking algorithm (141)

5.3 People counting algorithm (142)

5.4 Object detection under vertical view (144)

5.5 Multiple feature extraction (146)

5.5.1 Matching response features (146)

5.5.2 Motion intensity features (148)

5.5.3 Scale features (149)

5.6 People tracking and counting schemes (150)

5.6.1 The nearest neighbor-based tracking and counting principle (150)

5.6.2 Optimal color clustering-based MeanShift tracking and counting

principle (151)

5.7 Establishment of people counting system (154)

5.8 Experimental analysis (155)

5.8.1 Experiments results on reducing false alarms and detecting multiple

objects (156)

5.8.2 Experimental results on non-rush hour sequences (161)

5.8.3 Experimental results on rush hour sequences (161)

5.8.4 Experimental results on the sequences with cargo (162)

5.8.5 Experimental results on the sequences with lighting changs (162)

5.8.6 Experimental results of optimal color clustering (164)

5.8.7 Comparison results of all the methods on video datasets (168)

5.8.8 Activity analysis in people counting videos (169)

5.9 Summary of this chapter (170)

Conclusions (171)

References (174)

Papers published and other achievements in the period of Ph.D. education (191)

Statement of copyright and letter of authorization (193)

Acknowledgements (194)

Resume (196)