3D Hand Pose Estimation Based on Five-Layer Ensemble CNN
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School of Information Engineering, Nanchang University, Nanchang 330031, China
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Center of Computer, Nanchang University, Nanchang 330031, China
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School of Software, Jiangxi Agricultural University, Nanchang 330045, China
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State Key Lab of CAD & CG of Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 649; https://doi.org/10.3390/s21020649
Received: 28 December 2020
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Revised: 13 January 2021
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Accepted: 19 January 2021
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Published: 19 January 2021
(This article belongs to the Section Sensing and Imaging)
Estimating accurate 3D hand pose from a single RGB image is a highly challenging problem in pose estimation due to self-geometric ambiguities, self-occlusions, and the absence of depth information. To this end, a novel Five-Layer Ensemble CNN (5LENet) is proposed based on hierarchical thinking, which is designed to decompose the hand pose estimation task into five single-finger pose estimation sub-tasks. Then, the sub-task estimation results are fused to estimate full 3D hand pose. The hierarchical method is of great benefit to extract deeper and better finger feature information, which can effectively improve the estimation accuracy of 3D hand pose. In addition, we also build a hand model with the center of the palm (represented as Palm) connected to the middle finger according to the topological structure of hand, which can further boost the performance of 3D hand pose estimation. Additionally, extensive quantitative and qualitative results on two public datasets demonstrate the effectiveness of 5LENet, yielding new state-of-the-art 3D estimation accuracy, which is superior to most advanced estimation methods.
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MDPI and ACS Style
Fan, L.; Rao, H.; Yang, W. 3D Hand Pose Estimation Based on Five-Layer Ensemble CNN. Sensors 2021, 21, 649. https://doi.org/10.3390/s21020649
AMA Style
Fan L, Rao H, Yang W. 3D Hand Pose Estimation Based on Five-Layer Ensemble CNN. Sensors. 2021; 21(2):649. https://doi.org/10.3390/s21020649
Chicago/Turabian StyleFan, Lili, Hong Rao, and Wenji Yang. 2021. "3D Hand Pose Estimation Based on Five-Layer Ensemble CNN" Sensors 21, no. 2: 649. https://doi.org/10.3390/s21020649
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