Cascaded Hierarchical CNN for RGB-Based 3D Hand Pose Estimation

3D hand pose estimation can provide basic information about gestures, which has an important significance in the fields of Human-Machine Interaction (HMI) and Virtual Reality (VR). In recent years, 3D hand pose estimation from a single depth image has made great research achievements due to the deve...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-13
Main Authors: Fan, Lili, Yang, Wenji, Liu, Wei, Dai, Shiming, Zhang, Jihao
Format: Article
Language:English
Subjects:
Accuracy
Back propagation
Feature extraction
Mathematical problems
Methods
Neural networks
Palm
Pose estimation
Virtual reality
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Summary:3D hand pose estimation can provide basic information about gestures, which has an important significance in the fields of Human-Machine Interaction (HMI) and Virtual Reality (VR). In recent years, 3D hand pose estimation from a single depth image has made great research achievements due to the development of depth cameras. However, 3D hand pose estimation from a single RGB image is still a highly challenging problem. In this work, we propose a novel four-stage cascaded hierarchical CNN (4CHNet), which leverages hierarchical network to decompose hand pose estimation into finger pose estimation and palm pose estimation, extracts separately finger features and palm features, and finally fuses them to estimate 3D hand pose. Compared with direct estimation methods, the hand feature information extracted by the hierarchical network is more representative. Furthermore, concatenating various stages of the network for end-to-end training can make each stage mutually beneficial and progress. The experimental results on two public datasets demonstrate that our 4CHNet can significantly improve the accuracy of 3D hand pose estimation from a single RGB image.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/8432840