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

Vu, H, Wilkinson, R, Lech, M and Cheng, E 2017, 'A Comparison between Anatomy-Based and Data-Driven Tree Models for Human Pose Estimation', in Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017), Sydney, Australia, 29 November - 1 December 2017, pp. 1-5.


Document type: Conference Paper
Collection: Conference Papers

Title A Comparison between Anatomy-Based and Data-Driven Tree Models for Human Pose Estimation
Author(s) Vu, H
Wilkinson, R
Lech, M
Cheng, E
Year 2017
Conference name DICTA 2017
Conference location Sydney, Australia
Conference dates 29 November - 1 December 2017
Proceedings title Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017)
Publisher IEEE
Place of publication United States
Start page 1
End page 5
Total pages 5
Abstract 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.
Subjects Signal Processing
Keyword(s) Pose estimation
Kernel
Torso
Transforms
Message passing
Feature extraction
Training
DOI - identifier 10.1109/DICTA.2017.8227386
Copyright notice © 2017 IEEE
ISBN 9781538628409
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