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
Detecting, segmenting and tracking unknown objects using multi-label MRF inference
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: 0000-0003-0579-3372
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.
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
2014 (English)In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 118, 111-127 p.Article in journal (Refereed) Published
Abstract [en]

This article presents a unified framework for detecting, segmenting and tracking unknown objects in everyday scenes, allowing for inspection of object hypotheses during interaction over time. A heterogeneous scene representation is proposed, with background regions modeled as a combinations of planar surfaces and uniform clutter, and foreground objects as 3D ellipsoids. Recent energy minimization methods based on loopy belief propagation, tree-reweighted message passing and graph cuts are studied for the purpose of multi-object segmentation and benchmarked in terms of segmentation quality, as well as computational speed and how easily methods can be adapted for parallel processing. One conclusion is that the choice of energy minimization method is less important than the way scenes are modeled. Proximities are more valuable for segmentation than similarity in colors, while the benefit of 3D information is limited. It is also shown through practical experiments that, with implementations on GPUs, multi-object segmentation and tracking using state-of-art MRF inference methods is feasible, despite the computational costs typically associated with such methods.

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 118, 111-127 p.
Keyword [en]
Figure-ground segmentation, Active perception, MRF, Multi-object tracking, Object detection, GPU acceleration
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-133215DOI: 10.1016/j.cviu.2013.10.007ISI: 000328591500011Scopus ID: 2-s2.0-84890998700OAI: oai:DiVA.org:kth-133215DiVA: diva2:659983
Note

QC 20140122. Updated from accepted to published.

Available from: 2013-10-28 Created: 2013-10-28 Last updated: 2017-12-06Bibliographically approved

Open Access in DiVA

2011_CVIU_bbk(2533 kB)528 downloads
File information
File name FULLTEXT01.pdfFile size 2533 kBChecksum SHA-512
099f96787c96f7113b1497e57e134823744a15b41ab0ab9c031236fdd17ac6fd9bdc47d042d47d1e1ea4e4d1d84820b6de889fa437132b7de2084c2289d70e81
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Björkman, MårtenKragic, Danica

Search in DiVA

By author/editor
Björkman, MårtenBergström, NiklasKragic, Danica
By organisation
Computer Vision and Active Perception, CVAPCentre for Autonomous Systems, CAS
In the same journal
Computer Vision and Image Understanding
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 528 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 319 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