A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition
Hand gestures are a well-known and straightforward method of human-computer interaction. The majority of the study focused on hand gesture recognition. However, little work has been done to develop a complete set of gesture recognition applications. With the improvement of model feature extraction a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10121049/ |
| _version_ | 1797828239404564480 |
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| author | Mingyue Zhang Zhiheng Zhou Tianlei Wang Wenlve Zhou |
| author_facet | Mingyue Zhang Zhiheng Zhou Tianlei Wang Wenlve Zhou |
| author_sort | Mingyue Zhang |
| collection | DOAJ |
| description | Hand gestures are a well-known and straightforward method of human-computer interaction. The majority of the study focused on hand gesture recognition. However, little work has been done to develop a complete set of gesture recognition applications. With the improvement of model feature extraction ability and the increase in the number of model parameters, it is becoming more challenging to achieve a small memory footprint on mobile devices based on an ARM architecture or CPU devices based on x86 architecture. However, these existing methods are heavy, requiring more memory and inference time. The execution of memory-efficient CNNs without compromising accuracy has been a challenge, especially when the inference has to be performed on an edge computing device in real time. Therefore, we propose a lightweight network for hand gesture recognition (LHGR-Net) and deploy it on a Raspberry Pi. LHGR-Net consists of three main parts: the base network structure, the multiscale structure (MSS), and the lightweight attention structure (LAS). We present pre-trained weights that are learned from other data to initialize the network structure. In addition, the LHGR-Net model was made to be deployed on a Raspberry Pi, and a deployed model can be used to control home appliances. Extensive experiments show that our method achieves almost as good as state-of-the-art performance in hand gesture recognition and running time. |
| first_indexed | 2024-04-09T13:01:28Z |
| format | Article |
| id | doaj.art-3196d5cd90894e4d90110ab468a158d2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T13:01:28Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3196d5cd90894e4d90110ab468a158d22023-05-12T23:00:33ZengIEEEIEEE Access2169-35362023-01-0111454934550310.1109/ACCESS.2023.327371310121049A Lightweight Network Deployed on ARM Devices for Hand Gesture RecognitionMingyue Zhang0Zhiheng Zhou1https://orcid.org/0000-0003-4040-0175Tianlei Wang2Wenlve Zhou3https://orcid.org/0000-0002-7500-5581School of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaDepartment of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaHand gestures are a well-known and straightforward method of human-computer interaction. The majority of the study focused on hand gesture recognition. However, little work has been done to develop a complete set of gesture recognition applications. With the improvement of model feature extraction ability and the increase in the number of model parameters, it is becoming more challenging to achieve a small memory footprint on mobile devices based on an ARM architecture or CPU devices based on x86 architecture. However, these existing methods are heavy, requiring more memory and inference time. The execution of memory-efficient CNNs without compromising accuracy has been a challenge, especially when the inference has to be performed on an edge computing device in real time. Therefore, we propose a lightweight network for hand gesture recognition (LHGR-Net) and deploy it on a Raspberry Pi. LHGR-Net consists of three main parts: the base network structure, the multiscale structure (MSS), and the lightweight attention structure (LAS). We present pre-trained weights that are learned from other data to initialize the network structure. In addition, the LHGR-Net model was made to be deployed on a Raspberry Pi, and a deployed model can be used to control home appliances. Extensive experiments show that our method achieves almost as good as state-of-the-art performance in hand gesture recognition and running time.https://ieeexplore.ieee.org/document/10121049/Lightweightdeploymentmultiscale structurelightweight attention structurepre-trained weightsRaspberry Pi |
| spellingShingle | Mingyue Zhang Zhiheng Zhou Tianlei Wang Wenlve Zhou A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition IEEE Access Lightweight deployment multiscale structure lightweight attention structure pre-trained weights Raspberry Pi |
| title | A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition |
| title_full | A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition |
| title_fullStr | A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition |
| title_full_unstemmed | A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition |
| title_short | A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition |
| title_sort | lightweight network deployed on arm devices for hand gesture recognition |
| topic | Lightweight deployment multiscale structure lightweight attention structure pre-trained weights Raspberry Pi |
| url | https://ieeexplore.ieee.org/document/10121049/ |
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