Human Pose Estimation and Action Recognition Using Deep Neur

发布时间：2021-04-02 07:34

　　视频中的人体姿势、动作识别是人类行为自动分析理解的基本任务。无论在运动还是静止情况下,获取人体信息都必需进行人体姿势、动作识别。随着机器学习的快速发展和深度学习技术的进步,尤其是用于特征提取、分类或回归的端到端深度神经网络结构,成为提高图像和视频中姿势估计和动作识别性能的关键。在本论文中,我们提出了使用深度卷积神经网络进行姿态估计和动作识别的新技术,这是一种专门为二维特征提取而设计的深度神经网络。由于深度卷积神经网络能够自动学习训练数据中的低级和高级特征,基于深度卷积神经网络的方法优于此前基于特征工程的方法。由于在图像识别的关键是根据所需任务提取相关特征,因此在我们提出的技术中,重点是如何利用新的深度卷积神经网络结构来改进特征提取。我们从数据类型和问题性质两个不同方面解决问题。首先,我们将深度图像中的三维姿态估计和彩色图像中的二位姿态估计视为回归问题,在使用深度卷积神经模型进行端到端学习的过程中,我们将输入图像直接映射到姿势位置。其次,我们同时使用深度图像和三维姿势数据来构建提供不同类型的运动特征的两个描述符,然后设计了三个深度卷积神经网络通道用于特征提取和动作分类。最后,作为一项补充... 

【文章来源】：上海交通大学上海市 211工程院校 985工程院校 教育部直属院校

【文章页数】：140 页

【学位级别】：博士

【文章目录】：
摘要
Abstract
List of Abbreviations
Chapter 1 Introduction
    1.1 Background
    1.2 Thesis Objective
    1.3 Challenges
        1.3.1 RGB-D-Based3D Single Person Pose Estimation
        1.3.2 RGB-Based2D Single Person Pose Estimation
        1.3.3 RGB-D-Based and Posture-Based Action Recognition
        1.3.4 Posture-Based Motion Quantification
    1.4 Contributions
    1.5 Thesis Structure
Chapter 2 Related Work
    2.1 Human Pose Estimation
        2.1.1 RGB-D-Based3D Pose Estimation
        2.1.2 RGB-Based2D Single-Person Pose Estimation
    2.2 Human Action Recognition
        2.2.1 RGB-D-Based Action Recognition
        2.2.2 Skeleton-Based Action Recognition
    2.3 Wearable and Wireless Sensor-Based Pose Estimation and Action Recognition
    2.4 Human Motion Capture and Motion Comparison
Chapter 3 3D Human Pose Estimation From a Single Depth Image
    3.1 Overview
    3.2 Human Pose Estimation Method
        3.2.1 Data Normalisation
        3.2.2 Convolutional Neural Network Model
    3.3 Experimental Results
        3.3.1 Training and Testing
        3.3.2 Comparison and Discussion
Chapter 4 Single-Person2D Pose Estimation Using Hybrid Refinement-CorrectionHeatmaps
    4.1 Overview
    4.2 Hybrid Refinement-Correction Pose Estimation
        4.2.1 Pose Refinement
        4.2.2 Pose Correction
        4.2.3 Heatmaps Fusion
    4.3 Experimental Results
        4.3.1 Training and Testings Settings
        4.3.2 MPII Dataset
        4.3.3 FLIC Dataset
        4.3.4 Influence of the Correction Network（CNet）
        4.3.5 Computation Complexity
Chapter 5 Action-Fusion:Human Action Recognition Using Depth Images andBody Postures
    5.1 Overview
    5.2 Action Recognition Method
        5.2.1 Action Descriptors
        5.2.2 Convolutional Neural Network Model
        5.2.3 Score Fusion
    5.3 Experimental Results
        5.3.1 Datasets Results Evaluation
        5.3.2 Processing Computation Complexity
Chapter 6 Effective3D Joints-Based Human Motion Quantification and SimilarityEvaluation
    6.1 Overview
    6.2 Method
        6.2.1 Motion Quantification
        6.2.2 Motion Comparison
    6.3 Experimental Results
        6.3.1 UTD-MHAD Dataset
    6.4 User Movements Comparison Study Using Kinect
        6.4.1 Comparison with Existing Methods
Chapter 7 Conclusion
    7.1 Summary
        7.1.1 General Summary
        7.1.2 Detailed Summary
    7.2 Limitations and Possible Improvements
    7.3 Proposed Future Work
Bibliography
Acknowledgements
List of Publications



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