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
Visual Tracking Using Deep Motion Features
Linköping University, Department of Electrical Engineering, Computer Vision.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Visuell följning med hjälp av djup inlärning och optiskt flöde (Swedish)
Abstract [en]

Generic visual tracking is a challenging computer vision problem, where the position of a specified target is estimated through a sequence of frames. The only given information is the initial location of the target. Therefore, the tracker has to adapt and learn any kind of object, which it describes through visual features used to differentiate target from background. Standard appearance features only capture momentary visual information. This master’s thesis investigates the use of deep features extracted through optical flow images processed in a deep convolutional network. The optical flow is calculated using two consecutive images, and thereby captures the dynamic nature of the scene. Results show that this information is complementary to the standard appearance features, and improves performance of the tracker. Deep features are typically very high dimensional. Employing dimensionality reduction can increase both the efficiency and performance of the tracker. As a second aim in this thesis, PCA and PLS were evaluated and compared. The evaluations show that the two methods are almost equal in performance, with PLS actually receiving slightly better score than the popular PCA. The final proposed tracker was evaluated on three challenging datasets, and was shown to outperform other state-of-the-art trackers.

Place, publisher, year, edition, pages
2016. , p. 53
Keywords [en]
Visual tracking, tracking, optical flow, deep features, DCF, correlation filters, SRDCF, computer vision
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-134342ISRN: LiTH-ISY-EX--16/5005--SEOAI: oai:DiVA.org:liu-134342DiVA, id: diva2:1071737
Presentation
2016-10-25, Algoritmen, 16:00 (English)
Supervisors
Examiners
Available from: 2017-02-06 Created: 2017-02-06Bibliographically approved

Open Access in DiVA

fulltext(8945 kB)212 downloads
File information
File name FULLTEXT01.pdfFile size 8945 kBChecksum SHA-512
25efebc61820a8ef5e55f817bff1ae59dbc5d49f174e756e388722adcb2dfd73efc60af78e4fa1371a6fe64d311f946306e111e44c3ee739b2747787b789bcf0
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Gladh, Susanna
By organisation
Computer Vision
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 212 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: 1120 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