Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Robust tracking of unknown objects through adaptive size estimation and appearance learning
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2314-2880
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
Show others and affiliations
2016 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2016, 559-566 p.Conference paper, Published paper (Refereed)
Abstract [en]

This work employs an adaptive learning mechanism to perform tracking of an unknown object through RGBD cameras. We extend our previous framework to robustly track a wider range of arbitrarily shaped objects by adapting the model to the measured object size. The size is estimated as the object undergoes motion, which is done by fitting an inscribed cuboid to the measurements. The region spanned by this cuboid is used during tracking, to determine whether or not new measurements should be added to the object model. In our experiments we test our tracker with a set of objects of arbitrary shape and we show the benefit of the proposed model due to its ability to adapt to the object shape which leads to more robust tracking results.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 559-566 p.
Keyword [en]
Adaptive learning mechanism, Appearance learning, Arbitrary shape, Object model, Rgb-d cameras, Robust tracking, Size estimation, Unknown objects, Robotics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-197233DOI: 10.1109/ICRA.2016.7487179ISI: 000389516200070Scopus ID: 2-s2.0-84977519696ISBN: 9781467380263 (print)OAI: oai:DiVA.org:kth-197233DiVA: diva2:1052727
Conference
2016 IEEE International Conference on Robotics and Automation, ICRA 2016, 16 May 2016 through 21 May 2016
Note

QC 20161207

Available from: 2016-12-07 Created: 2016-11-30 Last updated: 2017-01-19Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Pieropan, AlessandroKragic, DanicaKjellström, Hedvig
By organisation
Centre for Autonomous Systems, CASComputer Vision and Active Perception, CVAP
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 25 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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