A Comparison between Anatomy-Based and Data-Driven Tree Models for Human Pose Estimation

Vu, HT; Wilkinson, RH; Lech, M; Cheng, E

A Comparison between Anatomy-Based and Data-Driven Tree Models for Human Pose Estimation

Vu, HT Wilkinson, RH Lech, M Cheng, E

Permalink

Publication Type:: Conference Proceeding
Citation:: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7
Issue Date:: 2017-12-19

Closed Access

	Filename	Description	Size
	08227386.pdf	Published version	394.26 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Vu, HT	en_US
dc.contributor.author	Wilkinson, RH	en_US
dc.contributor.author	Lech, M	en_US
dc.contributor.author	Cheng, E https://orcid.org/0000-0003-1632-8062	en_US
dc.date.issued	2017-12-19	en_US
dc.identifier.citation	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7	en_US
dc.identifier.isbn	9781538628393	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126131
dc.description.abstract	© 2017 IEEE. Tree structures are commonly used to model relationships between body parts for articulated Human Pose Estimation (HPE). Tree structures can be used to model relationships among feature maps of joints in a structured learning framework using Convolutional Neural Networks (CNNs). This paper proposes new data-driven tree models for HPE. The data-driven tree structures were obtained using the Chow-Liu Recursive Grouping (CLRG) algorithm, representing the joint distribution of human body joints and tested using the Leeds Sports Pose (LSP) dataset. The paper analyzes the effect of the variation of the number of nodes on the accuracy of the HPE. Experimental results showed that the data-driven tree model obtained 1% higher HPE accuracy compared to the traditional anatomy-based model. A further improvement of 0.5% was obtained by optimizing the number of nodes in the traditional anatomy-based model.	en_US
dc.relation.ispartof	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications	en_US
dc.relation.isbasedon	10.1109/DICTA.2017.8227386	en_US
dc.title	A Comparison between Anatomy-Based and Data-Driven Tree Models for Human Pose Estimation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-December	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2017-December	en_US

Abstract:

© 2017 IEEE. Tree structures are commonly used to model relationships between body parts for articulated Human Pose Estimation (HPE). Tree structures can be used to model relationships among feature maps of joints in a structured learning framework using Convolutional Neural Networks (CNNs). This paper proposes new data-driven tree models for HPE. The data-driven tree structures were obtained using the Chow-Liu Recursive Grouping (CLRG) algorithm, representing the joint distribution of human body joints and tested using the Leeds Sports Pose (LSP) dataset. The paper analyzes the effect of the variation of the number of nodes on the accuracy of the HPE. Experimental results showed that the data-driven tree model obtained 1% higher HPE accuracy compared to the traditional anatomy-based model. A further improvement of 0.5% was obtained by optimizing the number of nodes in the traditional anatomy-based model.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/126131