InterNet+: A Light Network for Hand Pose Estimation

Hand pose estimation from RGB images has always been a difficult task, owing to the incompleteness of the depth information. Moon et al. improved the accuracy of hand pose estimation by using a new network, InterNet, through their unique design. Still, the network still has potential for improvement...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 21; no. 20; p. 6747
Main Authors Liu, Yang, Jiang, Jie, Sun, Jiahao, Wang, Xianghan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 11.10.2021
MDPI
Subjects
Artificial intelligence
attention mechanism
Color imagery
Computer architecture
Computer vision
Datasets
Deep learning
Hand
hand pose estimation
Internet
Methods
neural network
Neural networks
Pose estimation
attention mechanism
hand pose estimation
neural network
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Summary:Hand pose estimation from RGB images has always been a difficult task, owing to the incompleteness of the depth information. Moon et al. improved the accuracy of hand pose estimation by using a new network, InterNet, through their unique design. Still, the network still has potential for improvement. Based on the architecture of MobileNet v3 and MoGA, we redesigned a feature extractor that introduced the latest achievements in the field of computer vision, such as the ACON activation function and the new attention mechanism module, etc. Using these modules effectively with our network, architecture can better extract global features from an RGB image of the hand, leading to a greater performance improvement compared to InterNet and other similar networks.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21206747