Author ORCID Identifier
Date Available
8-3-2017
Year of Publication
2017
Degree Name
Master of Science (MS)
Document Type
Master's Thesis
College
Engineering
Department/School/Program
Computer Science
First Advisor
Dr. Ruigang Yang
Abstract
This thesis introduces a 3D body tracking system based on neutral networks and 3D geometry, which can robustly estimate body poses and accurate body joints. This system takes RGB-D data as input. Body poses and joints are firstly extracted from color image using deep learning approach. The estimated joints and skeletons are further translated to 3D space by using camera calibration information. This system is running at the rate of 3 4 frames per second. It can be used to any RGB-D sensors, such as Kinect, Intel RealSense [14] or any customized system with color depth calibrated. Comparing to the sate-of-art 3D body tracking system, this system is more robust, and can get much more accurate joints locations, which will benefits projects require precise joints, such as virtual try-on, body measure, real-time avatar driven.
Digital Object Identifier (DOI)
https://doi.org/10.13023/ETD.2017.374
Recommended Citation
Xu, Qingguo, "3D Body Tracking using Deep Learning" (2017). Theses and Dissertations--Computer Science. 59.
https://uknowledge.uky.edu/cs_etds/59
