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Vehicle Classification via Multi-dimension Feature Extraction with Millimeter Wave Radar

  • Jianwen Wang
  • , Gang Li
  • , Jian Jiao
  • , Zhichun Zhao*
  • , Juan Li
  • *Corresponding author for this work
  • Tsinghua University

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

Abstract

Accurate vehicle classification is an important issue in autonomous driving and traffic monitoring technology. In order to improve the classification accuracy of road vehicles in passive traffic monitoring scenario with millimeter wave (MMW) radar, a vehicle classification algorithm based on multi-dimension feature extraction and support vector machine (SVM) is proposed in this paper. According to the scattering points distribution of the vehicle in range-doppler-angle domain, 9 statistical features are mined and extracted to obtain the feature vector, and then an SVM classifier is used to realize vehicle classification. The real data experimental results based on a 77GHz MMW radar verify the effectiveness and superiority of the proposed method in road vehicle classification over the classification method based on point cloud feature and SVM.

Original languageEnglish
Title of host publication2021 CIE International Conference on Radar, Radar 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1336-1339
Number of pages4
ISBN (Electronic)9781665498142
DOIs
Publication statusPublished - 2021
Event2021 CIE International Conference on Radar, Radar 2021 - Haikou, Hainan, China
Duration: 15 Dec 202119 Dec 2021

Publication series

NameProceedings of the IEEE Radar Conference
Volume2021-December
ISSN (Print)1097-5764
ISSN (Electronic)2375-5318

Conference

Conference2021 CIE International Conference on Radar, Radar 2021
Country/TerritoryChina
CityHaikou, Hainan
Period15/12/2119/12/21

Keywords

  • feature extraction
  • millimeter wave radar
  • support vector machine
  • vehicle classification

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