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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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基于频率调节的连续波雷达与元学习的行人姿势识别.

Jiajia Shi1, Qiang Zhang1, Quan Shi1

  • 1School of Transportation and Civil Engineering, Nantong University, Nantong 226001, China.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
概括

这项研究引入了使用毫米波雷达进行行人步行识别的先进模型. 该方法在识别个体方面实现了高精度,提高了自动驾驶的安全性.

关键词:
哺乳动物 哺乳动物角边缘损失函数的角度边缘损失函数道注意力机制的注意力机制微多普勒的微型多普勒.毫米波雷达是一种毫米波雷达.构成认可的认可.

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相关实验视频

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科学领域:

  • 机器人和人工智能机器人与人工智能
  • 信号处理 信号处理
  • 计算机视觉 计算机视觉

背景情况:

  • 在自主系统中对非侵入性监控的需求日益增加.
  • 需要使用像毫米波雷达这样的先进传感器进行强大的行人识别.

研究的目的:

  • 使用频率调制连续波 (FMCW) 毫米波雷达开发一种有效的行人步行识别模型.
  • 为了提高小样本微多普勒图像的特征提取和分类精度.

主要方法:

  • 提出了一个ArcFace SE注意力模型-不可知论的超级学习方法 (AS-MAML).
  • 集成道注意力机制和ArcFace损失到一个元学习框架中.
  • 利用FMCW毫米波雷达进行微多普勒图像生成和分析.

主要成果:

  • 在实验测试中达到94.5%的准确性,用于姿势估计和图像分类.
  • 在DIAT-μRadHAR数据集上表现出强的性能,分类准确率为85.9%.
  • 验证了模型在具有挑战性的小样本场景中的有效性.

结论:

  • 该AS-MAML方法显著改善了使用毫米波雷达的行人步行识别.
  • 该模型显示了在自动驾驶应用中提高安全性和监控的巨大潜力.
  • 注意力机制和ArcFace损失有效地提高了基于雷达的识别精度.