Human Action Recognition Using Dual-Stream Neural Networks with FMCW Radar

Yuqing Wu, Yong Li, Wei Cheng, Guangyu Lei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a approach to human action recognition utilizing FMCW radar. We propose a method that extracts range-time and velocity-time features from radar echoes and processes them using a dual-stream neural network for action classification. The improved Constant False Alarm Rate (CFAR) detection algorithm, combined with a Shape Profile Constraint - Feature Map (SPC-FM) generation method, significantly enhances feature extraction by removing noise. Experimental results demonstrate an average classification accuracy of 96.4% across six common actions, showcasing the method's robustness for applications in various domains, including medical monitoring and autonomous vehicle systems.

Original languageEnglish
Title of host publicationIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331515669
DOIs
StatePublished - 2024
Event2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 - Zhuhai, China
Duration: 22 Nov 202424 Nov 2024

Publication series

NameIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Conference

Conference2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Country/TerritoryChina
CityZhuhai
Period22/11/2424/11/24

Keywords

  • dual-stream neural network
  • feature extraction
  • FMCW Radar
  • human action recognition

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