@inproceedings{f81147d8fc9e4347aae1d3540deb3cf3,
title = "Optical unmarked motion capture technology based on depth network and binocular vision",
abstract = "This paper presents an optical unmarked motion capture method based on depth network and binocular vision. This method optimizes the marked motion capture technology, eliminating the need for additional markers to reduce the complexity of the motion capture system. At the same time, this paper also optimizes the human joint point coding method, which can obtain the sequence numbers and interdependence of 18 human joint points including the toes of the human body. Then we utilize the deep convolutional neural network to extract the coordinates of the two-view 2D human joint points. Through the binocular vision principle and the least squares method, the 3D coordinates of the human joint points are obtained. According to this, the human skeleton model is drawn to reflect the human body motion state.",
keywords = "Binocular vision algorithm, Deep convolutional neural network, Human joint points, Human skeleton model, Least square method",
author = "Ye Li and Wenjie Chen and Qing You and Yangyang Sun and Jing Li",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866442",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7550--7555",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}