Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Target Detection Based on Improved Hausdorff Distance Matching Algorithm for Millimeter-Wave Radar and Video Fusion.

Sensors (Basel, Switzerland)·2022
Same author

Two-year outcome of high-risk benign prostate hyperplasia patients treated with transurethral prostate resection by plasmakinetic or conventional procedure.

Urology·2012
Same author

Combination therapy improves exercise capacity and reduces risk of clinical worsening in patients with pulmonary arterial hypertension: a meta-analysis.

Journal of cardiovascular pharmacology·2012
Same author

Tailored design of architecturally controlled Pt nanoparticles with huge surface areas toward superior unsupported Pt electrocatalysts.

ACS applied materials & interfaces·2012
Same author

Primary non-Hodgkin's lymphoma of the skull base: a case report and literature review.

Clinical neurology and neurosurgery·2012
Same author

Mitigation of augmented extrasynaptic NMDAR signaling and apoptosis in cortico-striatal co-cultures from Huntington's disease mice.

Neurobiology of disease·2012

相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

2.7K

交叉模式监督的人体姿势识别技术 透墙雷达的识别技术

Dongpo Xu1,2, Yunqing Liu1,2, Qian Wang1

  • 1School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
概括

这项研究引入了一种新的跨模式监督方法,用于透墙雷达的人体姿势识别. 通过将摄像头和雷达数据与深度学习相结合,它可以在墙后实现准确的姿势识别,优于传统方法.

关键词:
跨模式监管 跨模式监管深度学习是一种深度学习.机器学习是机器学习.目标姿势识别 目标姿势识别通过墙壁的雷达.

更多相关视频

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K
Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.2K

相关实验视频

Last Updated: Jun 28, 2025

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

2.7K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K
Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.2K

科学领域:

  • 计算机视觉 计算机视觉
  • 雷达信号处理 雷达信号处理
  • 机器学习 机器学习

背景情况:

  • 透墙雷达人体姿势识别对于安全和监视至关重要.
  • 使用雷达和传统机器学习的现有方法在复杂的场景中扎.
  • 在墙后准确地识别姿势仍然是一个重大挑战.

研究的目的:

  • 为了开发一种先进的穿墙雷达人体姿势识别方法.
  • 在封闭的环境中提高姿势识别的准确性和稳定性.
  • 解决传统基于雷达的识别技术的局限性.

主要方法:

  • 提出了一种跨模式的监督学习方法,集成相机和雷达数据.
  • 为培训建立了一个新的跨模式数据集.
  • 设计了一个深度学习网络架构,用于特征提取和姿势识别.

主要成果:

  • 拟议的方法准确地识别了墙后的人类姿势 (例如,站立,缩).
  • 实验结果显示,与传统方法相比,性能优越.
  • 证明有效的姿势识别,即使在未知的墙壁障碍.

结论:

  • 深度学习与跨模式监督的整合提供了一种创新的解决方案,用于通过墙壁的姿势识别.
  • 这种方法显著提高了目标姿势识别的稳定性和准确性.
  • 介绍了透墙雷达技术的实际应用的新视角.