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基于中粒度模型的视频人体姿态估计

史青宣 邸慧军 陆耀 田学东

史青宣, 邸慧军, 陆耀, 田学东. 基于中粒度模型的视频人体姿态估计. 自动化学报, 2018, 44(4): 646-655. doi: 10.16383/j.aas.2018.c160847
引用本文: 史青宣, 邸慧军, 陆耀, 田学东. 基于中粒度模型的视频人体姿态估计. 自动化学报, 2018, 44(4): 646-655. doi: 10.16383/j.aas.2018.c160847
SHI Qing-Xuan, DI Hui-Jun, LU Yao, TIAN Xue-Dong. A Medium Granularity Model for Human Pose Estimation in Video. ACTA AUTOMATICA SINICA, 2018, 44(4): 646-655. doi: 10.16383/j.aas.2018.c160847
Citation: SHI Qing-Xuan, DI Hui-Jun, LU Yao, TIAN Xue-Dong. A Medium Granularity Model for Human Pose Estimation in Video. ACTA AUTOMATICA SINICA, 2018, 44(4): 646-655. doi: 10.16383/j.aas.2018.c160847

基于中粒度模型的视频人体姿态估计

doi: 10.16383/j.aas.2018.c160847
基金项目: 

河北省高等学校科学技术研究重点项目 ZD2017208

国家自然科学基金 61375075

国家自然科学基金 9142020013

国家自然科学基金 61273273

详细信息
    作者简介:

    史青宣  河北大学计算机科学与技术学院讲师.北京理工大学计算机学院博士研究生.主要研究方向为计算机视觉, 模式识别.E-mail:shiqingxuan@bit.edu.cn

    邸慧军  北京理工大学计算机学院讲师.主要研究方向为计算机视觉, 模式识别, 机器学习.E-mail:ajon@bit.edu.cn

    田学东  河北大学计算机科学与技术学院教授.主要研究方向为模式识别与图像处理.E-mail:txd@hbu.edu.cn

    通讯作者:

    陆耀  北京理工大学计算机学院教授.主要研究方向为神经网络, 图像和信号处理, 模式识别.本文通信作者.E-mail:vis_yl@bit.edu.cn

A Medium Granularity Model for Human Pose Estimation in Video

Funds: 

the Key Project of the Science and Technology Research Program in University of Hebei Province of China ZD2017208

National Natural Science Foundation of China 61375075

National Natural Science Foundation of China 9142020013

National Natural Science Foundation of China 61273273

More Information
    Author Bio:

     Lecturer at the School of Computer Science and Technology, Hebei University. Ph. D. candidate at the School of Computer Science, Beijing Institute of Technology. Her research interest covers computer vision, and pattern recognition

     Lecturer at the School of Computer Science, Beijing Institute of Technology. His research interest covers computer vision, pattern recognitio, and machine learning

     Professor at the School of Computer Science and Technology, Hebei University. His research interest covers pattern recognition and image processing

    Corresponding author: LU Yao  Professor at the School of Computer Science, Beijing Institute of Technology. His research interest covers neural network, image and signal processing, and pattern recognition. Corresponding author of this paper
  • 摘要: 人体姿态估计是计算机视觉领域的一个研究热点,在行为识别、人机交互等领域均有广泛的应用.本文综合粗、细粒度模型的优点,以人体部件轨迹片段为实体构建中粒度时空模型,通过迭代的时域和空域交替解析,完成模型的近似推理,为每一人体部件选择最优的轨迹片段,拼接融合形成最终的人体姿态序列估计.为准备高质量的轨迹片段候选,本文引入全局运动信息将单帧图像中的最优姿态检测结果传播到整个视频形成轨迹,然后将轨迹切割成互相交叠的固定长度的轨迹片段.为解决对称部件易混淆的问题,从概念上将模型中的对称部件合并,在保留对称部件间约束的前提下,消除空域模型中的环路.在三个数据集上的对比实验表明本文方法较其他视频人体姿态估计方法达到了更高的估计精度.
    1)  本文责任编委 王亮
  • 图  1  现有视频人体姿态估计方法采用的模型

    Fig.  1  The models used in video pose estimation

    图  2  中粒度时空模型

    Fig.  2  The medium granularity model

    图  4  不同方法的长时运动估计对比

    Fig.  4  Long-term performances of different motion estimation approaches

    图  3  不同方法的短时运动估计对比

    Fig.  3  Short-term performances of different motion estimation approaches

    图  5  基于中粒度模型的视频人体姿态估计方法示意图

    Fig.  5  Overview of the video pose estimation method based on medium granularity model

    图  6  时空模型分解为空域子模型和时域子模型

    Fig.  6  Sub-models of the full graphical model

    图  7  算法关键策略有效性测试结果

    Fig.  7  Examination of key modules

    图  8  UnusualPose数据集上的实验结果对比

    Fig.  8  Qualitative comparison on UnusualPose dataset

    图  9  FYDP数据集上的实验结果

    Fig.  9  Sample results on FYDP dataset

    图  10  Sub_Nbest数据集上的实验结果

    Fig.  10  Sample results on Sub_Nbest dataset

    表  1  UnusualPose视频集上的PCK评分对比

    Table  1  PCK on UnusualPose dataset

    MethodHeadShld.ElbowWristHipKneeAnkleAvg
    Nbest99.899.476.265.087.870.871.581.5
    UVA99.493.872.756.289.366.362.477.2
    PE_GM98.798.389.973.891.076.488.988.1
    Ours98.798.1 90.175.195.9 88.489.590.8
    下载: 导出CSV

    表  2  FYDP视频集上的PCK评分对比

    Table  2  PCK on FYDP dataset

    MethodHeadShld.ElbowWristHipKneeAnkleAvg
    Nbest95.789.775.259.183.381.479.580.6
    UVA96.291.778.460.385.483.879.282.1
    PE_GM98.489.280.960.584.4 89.383.783.8
    Ours97.993.4 84 63.188.488.984.485.7
    下载: 导出CSV

    表  3  Sub_Nbest视频集上的PCP评分对比

    Table  3  PCP on Sub_Nbest dataset

    MethodHeadTorsoU.A.L.A.U.L.L.L.
    Nbest10061.066.041.086.084.0
    SYM10069.085.042.091.089.0
    PE_GM10097.9 97.967.094.786.2
    HPEV10010093.065.092.094.0
    Ours10098.196.658.6 95.1 94.8
    下载: 导出CSV
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  • 收稿日期:  2016-12-27
  • 录用日期:  2017-07-12
  • 刊出日期:  2018-04-20

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