Dense-connected Stacked Hourglass Networks for Human Pose Estimation

H. Liu; C. Ding

doi:10.54254/2755-2721/38/20230534

Research Article

Open access

Published on 7 February 2024

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Liu,H.;Ding,C. (2024). Dense-connected Stacked Hourglass Networks for Human Pose Estimation. Applied and Computational Engineering,38,86-89.

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Dense-connected Stacked Hourglass Networks for Human Pose Estimation

H. Liu *^,1, C. Ding ²

¹ McGill University
² Georgia Institution of Technology

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/38/20230534

Abstract

The main idea of this project is to try to improve the accuracy of human pose estimation in previous models. The new model proposed is based on the Stacked Hourglass Network with new structures added. The new structures ensured that the preservation of features of the original data by adding connections across the network, which we refer to as a “Dense-connected Stacked Hourglass” network, and we expected the new structure and the feature preserved could be helpful in the later stages because the Stacked Hourglass network pools down to very low resolution, during which important information may be lost. The data sets used in the project are MPII Human Pose and FLIC (Frames Labelled in Cinema). The final results show that the proposed architecture is able to improve the estimation accuracy to certain extend in identifying head, wrist and hip, while further studies on the architecture and improvements are still required.

Keywords

Stacked Hourglass, Human Pose Estimation, Machine Learning

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References

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Cite this article

Liu,H.;Ding,C. (2024). Dense-connected Stacked Hourglass Networks for Human Pose Estimation. Applied and Computational Engineering,38,86-89.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

Conference website: https://2023.confmla.org/

ISBN：978-1-83558-301-2(Print) / 978-1-83558-302-9(Online)

Conference date: 18 October 2023

Editor：Mustafa İSTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.38

ISSN：2755-2721(Print) / 2755-273X(Online)

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