Continuous Human Activity Classification with Radar Point Clouds and Point Transformer Networks

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Abstract

Due to numerous benefits, radar is considered as an important sensor for human activity classification. The problem of classifying continuous sequences of activities of unconstrained duration has been studied in this work. To tackle this challenge, a radar data processing method utilizing point transformer networks has been proposed. The method has been experimentally verified on a dataset of human activities, and experiments have been performed to determine its optimal implementation. Promising preliminary results on a 9-class dataset show test accuracy and macro F-1 scores in the range of 83% and 73% respectively.
Original languageEnglish
Title of host publicationProceedings of the 2023 20th European Radar Conference (EuRAD)
PublisherIEEE
Pages302-305
Number of pages4
ISBN (Electronic)978-2-87487-074-3
ISBN (Print)979-8-3503-2246-0
DOIs
Publication statusPublished - 2023
Event2023 20th European Radar Conference (EuRAD) - Berlin, Germany
Duration: 20 Sept 202322 Sept 2023
Conference number: 20th

Publication series

Name20th European Radar Conference, EuRAD 2023

Conference

Conference2023 20th European Radar Conference (EuRAD)
Country/TerritoryGermany
CityBerlin
Period20/09/2322/09/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Human activity recognition
  • machine learning
  • radar
  • point cloud processing

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