liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6096-3648
2017 (engelsk)Inngår i: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10424, s. 55-67Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. However, this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations. In this paper, we propose a unified formulation for learning a deformable convolution filter. In our framework, the deformable filter is represented as a linear combination of sub-filters. Both the sub-filter coefficients and their relative locations are inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.

sted, utgiver, år, opplag, sider
Springer, 2017. Vol. 10424, s. 55-67
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-145373DOI: 10.1007/978-3-319-64689-3_5ISI: 000432085900005ISBN: 9783319646886 (tryckt)ISBN: 9783319646893 (digital)OAI: oai:DiVA.org:liu-145373DiVA, id: diva2:1185623
Konferanse
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
Merknad

Funding agencies: SSF (SymbiCloud); VR (EMC2) [2016-05543]; SNIC; WASP; Nvidia

Tilgjengelig fra: 2018-02-26 Laget: 2018-02-26 Sist oppdatert: 2018-10-16bibliografisk kontrollert

Open Access i DiVA

fulltext(1166 kB)85 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1166 kBChecksum SHA-512
fd4015af1850cfe1684fc107504b90a2a405b54972889e27c86c48c78c056cd8cbfbe49f91a8799530d57a95869256de94bdb55fd57326ac54aeabd42b559adf
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Personposter BETA

Johnander, JoakimDanelljan, MartinKhan, Fahad ShahbazFelsberg, Michael

Søk i DiVA

Av forfatter/redaktør
Johnander, JoakimDanelljan, MartinKhan, Fahad ShahbazFelsberg, Michael
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 85 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 245 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf