ShanghaiTech University Knowledge Management System
| Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning | |
| 2024-05 | |
| 发表期刊 | SENSORS
 |
| EISSN | 1424-8220 |
| 卷号 | 24期号:9 |
| 发表状态 | 已发表 |
| DOI | 10.3390/s24092932 |
| 摘要 | With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%. © 2024 by the authors. |
| 关键词 | Classification (of information) Continuous wave radar Frequency modulation Gesture recognition Image classification Angular margin loss function Attention mechanisms Channel attention mechanism MAML Margin loss functions Metalearning Micro-Doppler Millimeter-wave radar Millimetre-wave radar Pose recognition |
| URL | 查看原文 |
| 收录类别 | EI ; SCI |
| 语种 | 英语 |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| WOS类目 | Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| WOS记录号 | WOS:001220043800001 |
| 出版者 | Multidisciplinary Digital Publishing Institute (MDPI) |
| EI入藏号 | 20242016083940 |
| EI主题词 | Millimeter waves |
| EI分类号 | 711 Electromagnetic Waves ; 716.1 Information Theory and Signal Processing ; 716.2 Radar Systems and Equipment ; 723.2 Data Processing and Image Processing ; 903.1 Information Sources and Analysis |
| 原始文献类型 | Journal article (JA) |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/375712 |
| 专题 | 物质科学与技术学院_PI研究组_纪清清组 |
| 通讯作者 | Shi, Quan |
| 作者单位 | 1.School of Transportation and Civil Engineering, Nantong University, Nantong; 226001, China; 2.Center for Transformative Science, ShanghaiTech University, Shanghai; 201210, China; 3.Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney; NSW; 2050, Australia |
| 推荐引用方式 GB/T 7714 | Shi, Jiajia,Zhang, Qiang,Shi, Quan,et al. Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning[J]. SENSORS,2024,24(9). |
| APA | Shi, Jiajia,Zhang, Qiang,Shi, Quan,Chu, Liu,&Braun, Robin.(2024).Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.SENSORS,24(9). |
| MLA | Shi, Jiajia,et al."Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning".SENSORS 24.9(2024). |
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