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RSS-Net: weakly-supervised multi-class semantic segmentation with FMCW radar

Abstract:

This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using Frequency-Modulated Continuous-Wave scanning radar. We advocate radar over the traditional sensors used for this task as it operates at longer ranges and is substantially more robust to adverse weather and illumination conditions. We avoid laborious manual labelling by exploiting the largest radar-focused urban autonomy dataset collected to...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IV47402.2020.9304674

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6121-5839
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
IEEE Publisher's website
Journal:
Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV2020) Journal website
Issue:
2020
Pages:
431-436
Publication date:
2021-01-08
Acceptance date:
2020-04-01
Event title:
IEEE Intelligent Vehicles Symposium (IV)
Event location:
Las Vegas, Nevada, United States
Event website:
https://2020.ieee-iv.org/
Event start date:
2020-10-19T00:00:00Z
Event end date:
2020-11-13T00:00:00Z
DOI:
EISBN:
978-1-7281-6673-5
EISSN:
2642-7214
ISSN:
1931-0587
ISBN:
978-1-7281-6674-2
Language:
English
Keywords:
Pubs id:
1098455
Local pid:
pubs:1098455
Deposit date:
2020-04-03

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