Dynamic Gesture Recognition Combining Two-stream 3D Convolution with Attention Mechanisms

Fenhua WANG; Qiang ZHANG; Chao HUANG; Ran ZHANG

doi:10.11999/JEIT200065

Volume 43 Issue 5

May 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(5): 1389-1396

Fenhua WANG, Qiang ZHANG, Chao HUANG, Ran ZHANG. Dynamic Gesture Recognition Combining Two-stream 3D Convolution with Attention Mechanisms[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1389-1396. doi: 10.11999/JEIT200065

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Fenhua WANG, Qiang ZHANG, Chao HUANG, Ran ZHANG. Dynamic Gesture Recognition Combining Two-stream 3D Convolution with Attention Mechanisms[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1389-1396. doi: 10.11999/JEIT200065

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Dynamic Gesture Recognition Combining Two-stream 3D Convolution with Attention Mechanisms

doi: 10.11999/JEIT200065

Fenhua WANG^{1, 2, 3
,
,},
Qiang ZHANG¹,
Chao HUANG¹,
Ran ZHANG¹

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
3.
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China

Funds: The National Key Research and Development Project of China (2017YFB1400101-01), The Fundamental Research Funds for the Central Universities (FRF-BD-19-002A)

Received Date: 2020-01-16
Rev Recd Date: 2020-12-06

Available Online: 2020-12-18

Publish Date: 2021-05-18

Abstract

Abstract

Benefits from the progress of computer hardware and computing power, natural and simple dynamic gesture recognition gets a lot of attention in human-computer interaction. In view of the requirement of the accuracy of dynamic gesture recognition in human-computer interaction, a method of dynamic gesture recognition that combines Two-stream Inflated 3D (I3D) Convolution Neural Network (CNN) with the Convolutional Block Attention Module (CBAM-I3D) is proposed. In addition, relevant parameters and structures of the I3D network model are improved. In order to improve the convergence speed and stability of the model, the Batch Normalization (BN) technology is used to optimize the network, which shortens the training time of the optimized network. At the same time, experimental comparisons with various Two-stream 3D convolution methods on the open source Chinese Sign Language (CSL) recognition dataset are performed. The experimental results show that the proposed method can recognize dynamic gestures well, and the recognition rate reaches 90.76%, which is higher than other dynamic gesture recognition methods. The validity and feasibility of the proposed method are verified.
- Dynamic gesture recognition,
- Deep learning,
- Two-stream 3D Convolution Neural Network (CNN),
- Attention mechanism,
- Batch Normalization (BN) layer

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References(21)

References

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