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Jingdong book

复杂场景下人体动作识别

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Jingdong book

复杂场景下人体动作识别

Name: 复杂场景下人体动作识别
Brand: Jingdong book
SKU: 3118174131
Price: 18.20 USD
Availability: OutOfStock

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《复杂场景下人体动作识别》在对人体动作识别与行为理解学习研究的基础上，从实际应用角度出发编写而成。《复杂场景下人体动作识别》共7章，重点阐述在现实复杂场景（如人体表观变化、杂乱背景和视角变化等）下的人体动作识别技术和方法。第1章介绍人体动作识别的意义、典型应用和难点问题；第2章主要对人体动作识别研究现状进行介绍；第3章论述在杂乱背景和摄像机移动场景下的时空兴趣点检测方法；第4章介绍基于视频抖动检测算法的混合时空兴趣点检测；第5章介绍基于稀疏编码的时空金字塔匹配的人体动作识别方法；第6章介绍视角无关的人体动作识别方法；第7章介绍复杂场景下鲁棒的人体动作分类方法。
《复杂场景下人体动作识别》主要供图像处理与模式识别、计算机科学与技术、控制科学与工程、信息与通信工程等学科领域的研究人员、研究生以及工程技术人员参考使用。

Catalogue

第1章引言
1.1 应用背景
1.1.1 视频标注与检索
1.1.2 智能视频监控
1.1.3 人机交互
1.2 研究的难点
1.3 研究内容和结构安排
1.3.1 研究内容及工作进展
1.3.2 结构安排

第2章人体动作识别的相关综述
2.1 视频图像表示
2.1.1 全局表示
2.1.2 局部表示
2.2 人体动作建模与分类
2.2.1 维数约简
2.2.2 直接分类
2.2.3 状态空间模型方法
2.3 方法的特点
2.4 本章小结

第3章杂乱背景和摄像机移动下的时空兴趣点检测方法
3.1 引言
3.2 非线性各向异性扩散滤波器及分析
3.2.1 Perona-Malik扩散模型
3.2.2 非线性各向异性扩散滤波
3.3 杂乱背景和摄像机移动下的时空兴趣点检测
3.3.1 基于非线性各向异性扩散滤波的时空兴趣点检测算法
3.3.2 计算方法
3.4 实验与分析
3.4.1 实验数据集和实验设置
3.4.2 摄像机轻微晃动下的时空兴趣点检测方法比较
3.4.3 杂乱背景和摄像机快速运动下的时空兴趣点检测方法比较
3.4.4 计算复杂度分析
3.5 本章小结

第4章改进的视频抖动自适应的时空兴趣点检测方法
4.1 引言
4.2 视频抖动检测算法
4.2.1 视频画面抖动检测算法
4.2.2 金字塔Lucas-Kanade稀疏光流特征
4.2.3 稀疏光流的前一后向误差估计
4.2.4 基于光流运动熵的视频画面抖动检测算法
4.3 基于视频抖动检测算法的混合时空兴趣点检测
4.4 实验与分析
4.4.1 实验数据集和实验设置
4.4.2 视频抖动检测验证
4.4.3 静态背景下的时空兴趣点检测方法比较
4.4.4 摄像机轻微晃动下的时空兴趣点检测方法比较
4.4.5 杂乱背景和摄像机快速运动下的时空兴趣点检测方法比较
4.5 本章小结

第5章基于稀疏编码的时空金字塔匹配的人体动作识别
5.1 引言
5.2 稀疏编码
5.2.1 数学描述
5.2.2 稀疏编码算法
5.3 基于BoF的人体动作识别框架
5.4 基于稀疏编码的人体动作识别方法
5.4.1 特征检测和表示
5.4.2 基于稀疏编码的码书学习
5.4.3 基于max pooling的人体动作描述子
5.5 基于稀疏编码的时空金字塔匹配
5.5.1 时空金字塔匹配
5.5.2 基于max pooIing的时空金字塔匹配
5.6 实验与分析
5.6.1 实验设置
5.6.2 KTH数据集上的实验
5.6.3 YouTube数据集上的实验
5.7 本章小结

第6章视角无关的人体动作识别的研究
6.1 引言
6.2 视角无关的人体动作识别的研究现状
6.3 线性动态系统
6.3.1 模型参数估计
6.3.2 距离度量
6.4 基于线性动态系统的视角无关的人体动作识别
6.4.1 基于LDSs模型参数的局部描述子
6.4.2 模型参数空间中的码书学习
6.4.3 动作描述子计算
6.5 实验与分析
6.5.1 数据集与实验设置
6.5.2 单／多目识别
6.5.3 交叉视角的识别
6.5.4 基于BoF的视角无关的动作识别方法比较
6.6 本章小结

第7章复杂场景下鲁棒的人体动作分类方法
7.1 引言
7.2 超完备基的图像稀疏表示
7.3 基于稀疏表示的人体动作分类算法
7.3.1 人体动作的稀疏表示
7.3.2 基于l1最小化的稀疏表示求解方法
7.3.3 l1最小化优化算法
7.3.4 分类方法
7.4 在噪声、干扰、遮挡情况下的人体动作分类算法
7.5 实验与分析
7.5.1 基于l1与l2最小化算法的比较
7.5.2 分类方法SR与SVM、K-NN的比较
7.5.3 鲁棒性测试
7.6 本章小结
参考文献
名词索引

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