Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar
Hand gesture recognition with radar sensors is essential because they can detect gestures despite environmental factors like lighting, dust, and complex backgrounds. Considering the complexity of a system, it is challenging to design CNNs on CPU devices and realize the carry-on mid-air gesture recog...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10122553/ |
| _version_ | 1797437742684045312 |
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| author | Lihong Qiao Zhixin Li Bin Xiao Yucheng Shu Weisheng Li Xinbo Gao |
| author_facet | Lihong Qiao Zhixin Li Bin Xiao Yucheng Shu Weisheng Li Xinbo Gao |
| author_sort | Lihong Qiao |
| collection | DOAJ |
| description | Hand gesture recognition with radar sensors is essential because they can detect gestures despite environmental factors like lighting, dust, and complex backgrounds. Considering the complexity of a system, it is challenging to design CNNs on CPU devices and realize the carry-on mid-air gesture recognition. We propose a mid-air gesture recognition method based on a novel discriminant feature, and it be used as part of a measurement system of hand movements using an ultrawideband (UWB) radar. The Gesture-ProxylessNAS (GPNAS) is presented to enhance the adaptability of model search and overcome the challenge of the network's computational complexity. In order to fully extract local spatial discriminant features and prevent information loss, local binary pattern (LBP) encoders are utilized to extract local spatial information. In the meantime, multilayer ShuffleNet with depthwise separable convolution is used to gradually leverage high-level spatial features. The GPNAS module revisits the multilayer ShuffleNet's design spaces using an optimization problem, greatly reducing the network's parameters and computational complexity. According to experimental verification on real UWB hand gestures, the proposed framework provides more satisfactory recognition performance and efficiency with a deeper network structure and fewer parameters. The proposed hand gesture recognition system can recognize gestures with a promising accuracy of 96.52% on the UWB-gestures public dataset. |
| first_indexed | 2024-03-09T11:27:02Z |
| format | Article |
| id | doaj.art-e2b9047ab4d84a5995eef20bb1d04cb2 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-09T11:27:02Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-e2b9047ab4d84a5995eef20bb1d04cb22023-12-01T00:00:46ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01165144515310.1109/JSTARS.2023.327483010122553Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB RadarLihong Qiao0https://orcid.org/0000-0001-7722-0093Zhixin Li1Bin Xiao2https://orcid.org/0000-0001-8469-5302Yucheng Shu3https://orcid.org/0000-0002-5737-9571Weisheng Li4https://orcid.org/0000-0002-9033-8245Xinbo Gao5https://orcid.org/0000-0002-7985-0037School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of computer science, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing University of Posts and Telecommunications, Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Telecommunications and Posts, Chongqing, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaHand gesture recognition with radar sensors is essential because they can detect gestures despite environmental factors like lighting, dust, and complex backgrounds. Considering the complexity of a system, it is challenging to design CNNs on CPU devices and realize the carry-on mid-air gesture recognition. We propose a mid-air gesture recognition method based on a novel discriminant feature, and it be used as part of a measurement system of hand movements using an ultrawideband (UWB) radar. The Gesture-ProxylessNAS (GPNAS) is presented to enhance the adaptability of model search and overcome the challenge of the network's computational complexity. In order to fully extract local spatial discriminant features and prevent information loss, local binary pattern (LBP) encoders are utilized to extract local spatial information. In the meantime, multilayer ShuffleNet with depthwise separable convolution is used to gradually leverage high-level spatial features. The GPNAS module revisits the multilayer ShuffleNet's design spaces using an optimization problem, greatly reducing the network's parameters and computational complexity. According to experimental verification on real UWB hand gestures, the proposed framework provides more satisfactory recognition performance and efficiency with a deeper network structure and fewer parameters. The proposed hand gesture recognition system can recognize gestures with a promising accuracy of 96.52% on the UWB-gestures public dataset.https://ieeexplore.ieee.org/document/10122553/LBP encodermid-air gesture recognitionProxylessNASUWB radar |
| spellingShingle | Lihong Qiao Zhixin Li Bin Xiao Yucheng Shu Weisheng Li Xinbo Gao Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing LBP encoder mid-air gesture recognition ProxylessNAS UWB radar |
| title | Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar |
| title_full | Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar |
| title_fullStr | Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar |
| title_full_unstemmed | Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar |
| title_short | Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar |
| title_sort | gesture proxylessnas a lightweight network for mid air gesture recognition based on uwb radar |
| topic | LBP encoder mid-air gesture recognition ProxylessNAS UWB radar |
| url | https://ieeexplore.ieee.org/document/10122553/ |
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