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

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
Full metadata record
Files in This Item:
Filename Description Size
08227386.pdfPublished version394.26 kB
Adobe PDF
© 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: