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Mark yourself: Road marking segmentation via weakly-supervised annotations from multimodal data

Abstract:

This paper presents a weakly-supervised learning system for real-time road marking detection using images of complex urban environments obtained from a monocular camera. We avoid expensive manual labelling by exploiting additional sensor modalities to generate large quantities of annotated images in a weakly-supervised way, which are then used to train a deep semantic segmentation network. At run time, the road markings in the scene are detected in real time in a variety of traffic situations...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA.2018.8460952

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Maddern, W More by this author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Keble College
ORCID:
0000-0001-6562-8454
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2018-09-13
Acceptance date:
2017-12-01
DOI:
Pubs id:
pubs:942467
URN:
uri:845b68ff-3df0-40a1-b58d-e966c0715b67
UUID:
uuid:845b68ff-3df0-40a1-b58d-e966c0715b67
Local pid:
pubs:942467

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