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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:
Accepted
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
ORCID:
0000-0001-6121-5839
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Publisher:
IEEE Publisher's website
Journal:
Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV2020)
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:
2020-06-23T00:00:00Z
Event end date:
2020-06-26T00:00:00Z
Pubs id:
1098455
Local pid:
pubs:1098455

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