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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2023 20th European Radar Conference (EuRAD) |
| Publisher | IEEE |
| Pages | 302-305 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-2-87487-074-3 |
| ISBN (Print) | 979-8-3503-2246-0 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 2023 20th European Radar Conference (EuRAD) - Berlin, Germany Duration: 20 Sept 2023 → 22 Sept 2023 Conference number: 20th |
Publication series
| Name | 20th European Radar Conference, EuRAD 2023 |
|---|
Conference
| Conference | 2023 20th European Radar Conference (EuRAD) |
|---|---|
| Country/Territory | Germany |
| City | Berlin |
| Period | 20/09/23 → 22/09/23 |
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-careOtherwise 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