北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (12): 2463-2469.doi: 10.13700/j.bh.1001-5965.2019.0384

• 论文 • 上一篇    下一篇

基于视频的三维人体姿态估计

杨彬1,2, 李和平3, 曾慧1,2   

  1. 1. 北京科技大学 自动化学院, 北京 100083;
    2. 北京市工业波谱成像工程技术研究中心, 北京 100083;
    3. 中国科学院自动化研究所, 北京 100190
  • 收稿日期:2019-07-09 出版日期:2019-12-20 发布日期:2019-12-31
  • 通讯作者: 曾慧 E-mail:hzeng@ustb.edu.cn
  • 作者简介:杨彬 男,硕士研究生。主要研究方向:计算机视觉;李和平 男,博士, 副研究员。主要研究方向:模式识别、机器学习;曾慧 女,博士,副教授,硕士生导师。主要研究方向:模式识别、图像处理。
  • 基金资助:
    国家自然科学基金(61973029);中央高校基本科研业务费专项资金(FRF-BD-17-002A)

Three-dimensional human pose estimation based on video

YANG Bin1,2, LI Heping3, ZENG Hui1,2   

  1. 1. School of Automation&Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
    2. Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China;
    3. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2019-07-09 Online:2019-12-20 Published:2019-12-31
  • Supported by:
    National Natural Science Foundation of China (61973029); the Fundamental Research Funds for the Central Universities (FRF-BD-17-002A)

摘要: 已有的三维人体姿态估计方法侧重于通过单帧图像来估计人体的三维姿态,忽略了视频中前后帧之间的相关性,因此,通过挖掘视频在时间维度上的信息可以进一步提高三维人体姿态估计的准确率。基于此,设计了一种可以充分提取视频时序信息的卷积神经网络结构,在获得高精度的同时也具有消耗计算资源小的优点,仅仅使用二维关节点的坐标为输入即可恢复完整的三维人体姿态。然后提出了一种新的损失函数利用相邻帧间人体姿态的连续性,来改进视频序列中三维姿态估计的平滑性,同时也解决了因缺少帧间信息而导致准确率下降的问题。通过在公开数据集Human3.6M上进行测试,实验结果表明本文方法相比目前的基准三维姿态估计算法的平均测试误差降低了1.2 mm,对于视频序列的三维人体姿态估计有着较高的准确率。

关键词: 三维人体姿态, 卷积神经网络, 视频序列, 损失函数, 平滑

Abstract: The existing 3D human pose estimation method focuses on estimating the 3D pose of the human body through a single frame image, while ignoring the correlation between the front and back frames in the video. Therefore, by investigating the information of the video in the time dimension, the accuracy of the 3D human pose estimation can be further improved. Based on this, the convolutional neural network structure that can fully extract the temporal information in the video is designed. It has the advantage of low computational resources and high precision. The complete 3D human pose can be restored only by using the coordinates of the 2D articulation point as input. Furthermore, a new loss function is proposed, which uses the continuity of human pose between adjacent frames to improve the smoothness of 3D pose estimation in video sequences, and also solves the problem of accuracy degradation due to lack of inter-frame information. By testing on the Human 3.6M dataset, the experimental results indicate that the average test error of the proposed method is 1.2 mm lower than that of the current standard 3D pose estimation algorithm, and the proposed method has a high accuracy for the 3D human pose estimation of video sequences.

Key words: three-dimensional human pose, convolutional neural network, video sequences, loss function, smoothness

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