University of Illinois Urbana-Champaign

Stacked dense-hourglass networks for human pose estimation

Wang, Dongbo

Content Files
WANG-THESIS-2018.pdf

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https://hdl.handle.net/2142/101155

Description

Title
Stacked dense-hourglass networks for human pose estimation
Author(s)
Wang, Dongbo
Issue Date
2018-04-12
Director of Research (if dissertation) or Advisor (if thesis)
Schwing, Alexander
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-09-04T20:34:02Z
Keyword(s)
  • Stacked Hourglass Networks
  • DenseNets
  • Pose Estimation
Abstract
Convolutional Neural Networks (CNNs) are driving major advances in many computer vision tasks, including the problem of 2D single-person pose estimation. For this task, the Stacked Hourglass Networks (Stack-HgNets) is one of the state-of-the-art architecture that uses residual modules extensively as the basic building block. The residual modules are well recognized for creating shortcut connections, skipping one or more layers which allows information and gradients to flow more effectively through a deep network without vanishing. In this work, we build on the Stack-HgNets and introduce the Stacked Dense-Hourglass Networks (Stack-DenseHgNets). They use dense blocks instead of the residual modules as the basic building block. The dense blocks create more direct connections between each layer and its subsequent successors, granting later filters the access to all the preceding feature-maps inside the same block. Therefore, dense blocks serve as the upgraded substitution for the residual modules. We evaluate the Stack-DenseHgNets on the popular human pose estimation benchmark dataset and compare its performance to the original Stack-HgNets. Using fewer parameters, the Stack-DenseHgNets obtains a performance competitive to the state-of-the-art results on the MPII Human Pose Dataset.
Graduation Semester
2018-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/101155
Copyright and License Information
Copyright 2018 Dongbo Wang

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