liu.seSearch for publications in DiVA
Change search
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
Evaluating Template Rescaling in Short-Term Single-Object Tracking
Linköping University, Department of Electrical Engineering, Information Coding. 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
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
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, short-term single-object tracking has emerged has a popular research topic, as it constitutes the core of more general tracking systems. Many such tracking methods are based on matching a part of the image with a template that is learnt online and represented by, for example, a correlation filter or a distribution field. In order for such a tracker to be able to not only find the position, but also the scale, of the tracked object in the next frame, some kind of scale estimation step is needed. This step is sometimes separate from the position estimation step, but is nevertheless jointly evaluated in de facto benchmarks. However, for practical as well as scientific reasons, the scale estimation step should be evaluated separately – for example,theremightincertainsituationsbeothermethodsmore suitable for the task. In this paper, we describe an evaluation method for scale estimation in template-based short-term single-object tracking, and evaluate two state-of-the-art tracking methods where estimation of scale and position are separable.

Place, publisher, year, edition, pages
IEEE , 2015.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-121356DOI: 10.1109/AVSS.2015.7301745ISI: 000380619700025ISBN: 9781467376327 (print)OAI: oai:DiVA.org:liu-121356DiVA: diva2:853786
Conference
17th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Karlsruhe, Germany, August 25, 2015
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784
Available from: 2015-09-15 Created: 2015-09-15 Last updated: 2016-12-08Bibliographically approved

Open Access in DiVA

fulltext(513 kB)262 downloads
File information
File name FULLTEXT02.pdfFile size 513 kBChecksum SHA-512
262258703aa4e37dc7580afbe5f8dc99aa7667179788d79d9ccf57bc588e981123ed43105fac13c10105b90a1be232ca0577a56b2646211b73bc21ebd6a24fb6
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Ahlberg, JörgenBerg, Amanda

Search in DiVA

By author/editor
Ahlberg, JörgenBerg, Amanda
By organisation
Information CodingComputer VisionFaculty of Science & Engineering
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 262 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
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 767 hits
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