@inproceedings{9f330ec061e04ed09db1304254506f92,
title = "3D Human Motion Capture Based on Neural Network and Triangular Gaussian Point Cloud",
abstract = "In this paper, an optical unmarked motion capture method based on convolutional neural network and triangular gaussian point cloud is proposed to achieve accurate 3D human pose estimation. Firstly, the Direct Linear Transformation(DLT) method is used to calibrate the actual multi camera system and obtain the internal and external parameters of all cameras. Then the depth neural network Cascaded Pyramid Network(CPN) is used to extract the 2D human key points in the images collected by each camera in the system. Next the triangle positioning and the least square method are used to preliminarily obtain the 3D human key point coordinates, and then the 3D key points of human body are optimized by gauss point cloud method to get the accurate 3D results of human body.",
keywords = "Depth Neural Network, Gaussian Point Cloud, Least Square, Pose Estimation, Triangulation",
author = "Qing You and Wenjie Chen and Ye Li",
note = "Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9188413",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7481--7486",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
address = "United States",
}