liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-6096-3648
University of Ljubljana, Slovenia.
Czech Technical University, Czech Republic.
University of Birmingham, England.
Show others and affiliations
2016 (English)In: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II, SPRINGER INT PUBLISHING AG , 2016, Vol. 9914, 824-849 p.Conference paper, Published paper (Refereed)
Abstract [en]

The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

Place, publisher, year, edition, pages
SPRINGER INT PUBLISHING AG , 2016. Vol. 9914, 824-849 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 9914
Keyword [en]
Performance evaluation; Object tracking; Thermal IR; VOT
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-133773DOI: 10.1007/978-3-319-48881-3_55ISI: 000389501700055ISBN: 978-3-319-48881-3 (print)ISBN: 978-3-319-48880-6 (print)OAI: oai:DiVA.org:liu-133773DiVA: diva2:1063949
Conference
14th European Conference on Computer Vision (ECCV)
Available from: 2017-01-11 Created: 2017-01-09 Last updated: 2017-01-22

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Felsberg, MichaelHäger, GustavBerg, AmandaEldesokey, AbdelrahmanAhlberg, JörgenKhan, FahadDanelljan, Martin
By organisation
Computer VisionFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)Department of Electrical Engineering
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 149 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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