University of Illinois Urbana-Champaign

3D human pose estimation using part affinity field

Zhao, Zixu

Content Files
ECE499-Sp2018-zhao-Zixu.pdf

Permalink

https://hdl.handle.net/2142/100062

Description

Title
3D human pose estimation using part affinity field
Author(s)
Zhao, Zixu
Contributor(s)
Huang, Thomas S.
Issue Date
2018-05
Keyword(s)
  • Computer Vision
  • Pose Estimation
  • Neural Network
Date of Ingest
2018-05-30T13:48:53Z
Abstract
Nowadays, following the success of deep learning in the Computer Vision field, many research studies are underway to produce state-of-the-art technologies that can predict 3D human poses given raw image pixels. These end-to-end systems create possibilities for future studies such as human pose or gait recognition, and their practical values in industry are beyond imagination. This thesis proposes an end-to-end system that predicts human joint locations in 3D space using only the raw image pixels as inputs. While the most used state-of-the-art method proposes that lifting joint locations from camera space to 3D space can be done in a simple and effective way only using 2D joint locations as inputs, our proposed system is even more effective and accurate with the help of part affinity fields.
Type of Resource
text
Genre of Resource
Other
Language
en
Permalink
http://hdl.handle.net/2142/100062

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