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
An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.ORCID iD: 0000-0002-6591-9400
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.ORCID iD: 0000-0002-6763-5487
2016 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

The Thermal Infrared Visual Object Tracking (VOT-TIR2015) Challenge was organized in conjunction with ICCV2015. It was the first benchmark on short-term,single-target tracking in thermal infrared (TIR) sequences. The challenge aimed at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. It was based on the VOT2013 Challenge, but introduced the following novelties: (i) the utilization of the LTIR (Linköping TIR) dataset, (ii) adaption of the VOT2013 attributes to thermal data, (iii) a similar evaluation to that of VOT2015. This paper provides an overview of the VOT-TIR2015 Challenge as well as the results of the 24 participating trackers.

Place, publisher, year, edition, pages
2016.
Series
Svenska sällskapet för automatiserad bildanalys (SSBA)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127598OAI: oai:DiVA.org:liu-127598DiVA: diva2:925830
Conference
Swedish Symposium on Image Analysis
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2016-06-10Bibliographically approved

Open Access in DiVA

fulltext(958 kB)261 downloads
File information
File name FULLTEXT01.pdfFile size 958 kBChecksum SHA-512
43fba7b832c8ee72b291215459b5da74991262ac6ccec3a9b3b05a946da872125d4bf2ab25ce0230abfa40b3f6f23733641f45a7139af22faba411780388cffa
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Berg, AmandaFelsberg, MichaelHäger, GustavAhlberg, Jörgen
By organisation
Computer VisionFaculty of Science & Engineering
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 261 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

urn-nbn

Altmetric score

urn-nbn
Total: 983 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