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Deformable Pose Network: A Multi-Stage Deformable Convolutional Network for 2D Hand Pose Estimation

Topics: Deep Learning for Tracking; Deep Learning for Visual Understanding ; Features Extraction; Human and Computer Interaction; Image Enhancement and Restoration; Image Registration; Machine Learning Technologies for Vision; Object Detection and Localization; Vision for Robotics

Authors: Sartaj Salman 1 ; Ali Zakir 1 and Hiroki Takahashi 1 ; 2

Affiliations: 1 Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan ; 2 Artificial Intelligence Exploration/Meta-Networking Research Center, The University of Electro-Communications, Tokyo, Japan

Keyword(s): Deformable Convolution, Multi-Stage DC, EfficientNet, 2D HPE.

Abstract: Hand pose estimation undergoes a significant advancement with the evolution of Convolutional Neural Networks (CNNs) in the field of computer vision. However, existing CNNs fail in many scenarios in learning the unknown transformations and geometrical constraints along with the other existing challenges for accurate estimation of hand keypoints. To tackle these issues we proposed a multi-stage deformable convolutional network for accurate 2D hand pose estimation from monocular RGB images while considering the computational complexity. We utilized EfficientNet as a backbone due to its powerful feature extraction capability, and deformable convolution to learn about the geometrical constraints. Our proposed model called Deformable Pose Network (DPN) outperforms in predicting the 2D keypoints in complex scenarios. Our analysis on the Panoptic studio hand dataset shows that our proposed model improves the accuracy by 2.36% and 7.29% as compared to existing methods i.e., OCPM and CPM respe ctively. (More)

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Paper citation in several formats:
Salman, S.; Zakir, A. and Takahashi, H. (2024). Deformable Pose Network: A Multi-Stage Deformable Convolutional Network for 2D Hand Pose Estimation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 814-821. DOI: 10.5220/0012569000003660

@conference{visapp24,
author={Sartaj Salman. and Ali Zakir. and Hiroki Takahashi.},
title={Deformable Pose Network: A Multi-Stage Deformable Convolutional Network for 2D Hand Pose Estimation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={814-821},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012569000003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Deformable Pose Network: A Multi-Stage Deformable Convolutional Network for 2D Hand Pose Estimation
SN - 978-989-758-679-8
IS - 2184-4321
AU - Salman, S.
AU - Zakir, A.
AU - Takahashi, H.
PY - 2024
SP - 814
EP - 821
DO - 10.5220/0012569000003660
PB - SciTePress