Stick it! Articulated tracking using spatial rigid object priors

Søren Hauberg; Kim Steenstrup Pedersen

doi:10.1007/978-3-642-19318-7_59

Stick it! Articulated tracking using spatial rigid object priors

Søren Hauberg, Kim Steenstrup Pedersen

Datalogisk Institut

9 Citationer (Scopus)

Abstract

Articulated tracking of humans is a well-studied field, but most work has treated the humans as being independent of the environment. Recently, Kjellström et al. [1] showed how knowledge of interaction with a known rigid object provides constraints that lower the degrees of freedom in the model. While the phrased problem is interesting, the resulting algorithm is computationally too demanding to be of practical use. We present a simple and elegant model for describing this problem. The resulting algorithm is computationally much more efficient, while it at the same time produces superior results.

Originalsprog	Engelsk
Titel	Computer Vision – ACCV 2010 : 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III
Redaktører	Ron Kimmel, Reinhard Klette, Akihiro Sugimoto
Antal sider	12
Vol/bind	III
Forlag	Springer
Publikationsdato	2011
Sider	758-769
ISBN (Trykt)	978-3-642-19317-0
ISBN (Elektronisk)	978-3-642-19318-7
DOI	https://doi.org/10.1007/978-3-642-19318-7_59
Status	Udgivet - 2011
Begivenhed	10th Asian Conference on Computer Vision - Queenstown, New Zealand Varighed: 8 nov. 2010 → 12 nov. 2010 Konferencens nummer: 10

Konference

Konference	10th Asian Conference on Computer Vision
Nummer	10
Land/Område	New Zealand
By	Queenstown
Periode	08/11/2010 → 12/11/2010

Navn	Lecture notes in computer science
Vol/bind	6494
ISSN	0302-9743

Adgang til dokumentet

10.1007/978-3-642-19318-7_59

Citationsformater

Hauberg, S., & Pedersen, K. S. (2011). Stick it! Articulated tracking using spatial rigid object priors. I R. Kimmel, R. Klette, & A. Sugimoto (red.), Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III (Bind III, s. 758-769). Springer. Lecture notes in computer science Bind 6494 https://doi.org/10.1007/978-3-642-19318-7_59

Stick it! Articulated tracking using spatial rigid object priors. / Hauberg, Søren; Pedersen, Kim Steenstrup.

Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III. red. / Ron Kimmel; Reinhard Klette; Akihiro Sugimoto. Bind III Springer, 2011. s. 758-769 (Lecture notes in computer science, Bind 6494).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Hauberg, S & Pedersen, KS 2011, Stick it! Articulated tracking using spatial rigid object priors. i R Kimmel, R Klette & A Sugimoto (red), Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III. bind III, Springer, Lecture notes in computer science, bind 6494, s. 758-769, 10th Asian Conference on Computer Vision, Queenstown, New Zealand, 08/11/2010. https://doi.org/10.1007/978-3-642-19318-7_59

Hauberg S, Pedersen KS. Stick it! Articulated tracking using spatial rigid object priors. I Kimmel R, Klette R, Sugimoto A, red., Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III. Bind III. Springer. 2011. s. 758-769. (Lecture notes in computer science, Bind 6494). doi: 10.1007/978-3-642-19318-7_59

Hauberg, Søren ; Pedersen, Kim Steenstrup. / Stick it! Articulated tracking using spatial rigid object priors. Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III. red. / Ron Kimmel ; Reinhard Klette ; Akihiro Sugimoto. Bind III Springer, 2011. s. 758-769 (Lecture notes in computer science, Bind 6494).

@inproceedings{7e4378f98d394647862b3aa56abd37c3,

title = "Stick it! Articulated tracking using spatial rigid object priors",

abstract = "Articulated tracking of humans is a well-studied field, but most work has treated the humans as being independent of the environment. Recently, Kjellstr{\"o}m et al. [1] showed how knowledge of interaction with a known rigid object provides constraints that lower the degrees of freedom in the model. While the phrased problem is interesting, the resulting algorithm is computationally too demanding to be of practical use. We present a simple and elegant model for describing this problem. The resulting algorithm is computationally much more efficient, while it at the same time produces superior results.",

author = "S{\o}ren Hauberg and Pedersen, {Kim Steenstrup}",

note = "Video material available at: http://image.diku.dk/HUMIM/accv2010/; 10th Asian Conference on Computer Vision ; Conference date: 08-11-2010 Through 12-11-2010",

year = "2011",

doi = "10.1007/978-3-642-19318-7_59",

language = "English",

isbn = "978-3-642-19317-0",

volume = "III",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "758--769",

editor = "Ron Kimmel and Reinhard Klette and Akihiro Sugimoto",

booktitle = "Computer Vision – ACCV 2010",

}

TY - GEN

T1 - Stick it! Articulated tracking using spatial rigid object priors

AU - Hauberg, Søren

AU - Pedersen, Kim Steenstrup

N1 - Conference code: 10

PY - 2011

Y1 - 2011

N2 - Articulated tracking of humans is a well-studied field, but most work has treated the humans as being independent of the environment. Recently, Kjellström et al. [1] showed how knowledge of interaction with a known rigid object provides constraints that lower the degrees of freedom in the model. While the phrased problem is interesting, the resulting algorithm is computationally too demanding to be of practical use. We present a simple and elegant model for describing this problem. The resulting algorithm is computationally much more efficient, while it at the same time produces superior results.

AB - Articulated tracking of humans is a well-studied field, but most work has treated the humans as being independent of the environment. Recently, Kjellström et al. [1] showed how knowledge of interaction with a known rigid object provides constraints that lower the degrees of freedom in the model. While the phrased problem is interesting, the resulting algorithm is computationally too demanding to be of practical use. We present a simple and elegant model for describing this problem. The resulting algorithm is computationally much more efficient, while it at the same time produces superior results.

U2 - 10.1007/978-3-642-19318-7_59

DO - 10.1007/978-3-642-19318-7_59

M3 - Article in proceedings

SN - 978-3-642-19317-0

VL - III

T3 - Lecture notes in computer science

SP - 758

EP - 769

BT - Computer Vision – ACCV 2010

A2 - Kimmel, Ron

A2 - Klette, Reinhard

A2 - Sugimoto, Akihiro

PB - Springer

T2 - 10th Asian Conference on Computer Vision

Y2 - 8 November 2010 through 12 November 2010

ER -