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Instance-Level Segmentation of Vehicles by Deep Contours
Goethe Univ, Germany.
Goethe Univ, Germany.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe Univ, Germany.
2017 (English)In: COMPUTER VISION - ACCV 2016 WORKSHOPS, PT I, SPRINGER INTERNATIONAL PUBLISHING AG , 2017, Vol. 10116, p. 477-492Conference paper, Published paper (Refereed)
Abstract [en]

The recognition of individual object instances in single monocular images is still an incompletely solved task. In this work, we propose a new approach for detecting and separating vehicles in the context of autonomous driving. Our method uses the fully convolutional network (FCN) for semantic labeling and for estimating the boundary of each vehicle. Even though a contour is in general a one pixel wide structure which cannot be directly learned by a CNN, our network addresses this by providing areas around the contours. Based on these areas, we separate the individual vehicle instances. In our experiments, we show on two challenging datasets (Cityscapes and KITTI) that we achieve state-of-the-art performance, despite the usage of a subsampling rate of two. Our approach even outperforms all recent works w.r.t. several rating scores.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG , 2017. Vol. 10116, p. 477-492
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-145823DOI: 10.1007/978-3-319-54407-6_32ISI: 000425842200032ISBN: 978-3-319-54407-6 (electronic)ISBN: 978-3-319-54406-9 (print)OAI: oai:DiVA.org:liu-145823DiVA, id: diva2:1192153
Conference
13th Asian Conference on Computer Vision (ACCV)
Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-03-21

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf