Details
| Original language | English |
|---|---|
| Title of host publication | Computer Vision - ECCV 2018 Workshops |
| Subtitle of host publication | Munich, Germany, September 8-14, 2018, Proceedings, Part IV |
| Editors | Laura Leal-Taixé, Stefan Roth |
| Place of Publication | Cham |
| Pages | 31-47 |
| Number of pages | 17 |
| Edition | 1. |
| ISBN (electronic) | 978-3-030-11018-5 |
| Publication status | Published - 23 Jan 2019 |
| Event | 15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: 8 Sept 2018 → 14 Sept 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11132 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
This paper deals with motion capture of kinematic chains (e.g. human skeletons) from monocular image sequences taken by uncalibrated cameras. We present a method based on projecting an observation onto a kinematic chain space (KCS). An optimization of the nuclear norm is proposed that implicitly enforces structural properties of the kinematic chain. Unlike other approaches our method is not relying on training data or previously determined constraints such as particular body lengths. The proposed algorithm is able to reconstruct scenes with little or no camera motion and previously unseen motions. It is not only applicable to human skeletons but also to other kinematic chains for instance animals or industrial robots. We achieve state-of-the-art results on different benchmark databases and real world scenes.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Computer Vision - ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part IV. ed. / Laura Leal-Taixé; Stefan Roth. 1. ed. Cham, 2019. p. 31-47 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11132 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A kinematic chain space for monocular motion capture
AU - Wandt, Bastian
AU - Ackermann, Hanno
AU - Rosenhahn, Bodo
PY - 2019/1/23
Y1 - 2019/1/23
N2 - This paper deals with motion capture of kinematic chains (e.g. human skeletons) from monocular image sequences taken by uncalibrated cameras. We present a method based on projecting an observation onto a kinematic chain space (KCS). An optimization of the nuclear norm is proposed that implicitly enforces structural properties of the kinematic chain. Unlike other approaches our method is not relying on training data or previously determined constraints such as particular body lengths. The proposed algorithm is able to reconstruct scenes with little or no camera motion and previously unseen motions. It is not only applicable to human skeletons but also to other kinematic chains for instance animals or industrial robots. We achieve state-of-the-art results on different benchmark databases and real world scenes.
AB - This paper deals with motion capture of kinematic chains (e.g. human skeletons) from monocular image sequences taken by uncalibrated cameras. We present a method based on projecting an observation onto a kinematic chain space (KCS). An optimization of the nuclear norm is proposed that implicitly enforces structural properties of the kinematic chain. Unlike other approaches our method is not relying on training data or previously determined constraints such as particular body lengths. The proposed algorithm is able to reconstruct scenes with little or no camera motion and previously unseen motions. It is not only applicable to human skeletons but also to other kinematic chains for instance animals or industrial robots. We achieve state-of-the-art results on different benchmark databases and real world scenes.
UR - http://www.scopus.com/inward/record.url?scp=85061708581&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1702.00186
DO - 10.48550/arXiv.1702.00186
M3 - Conference contribution
AN - SCOPUS:85061708581
SN - 978-3-030-11017-8
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 31
EP - 47
BT - Computer Vision - ECCV 2018 Workshops
A2 - Leal-Taixé, Laura
A2 - Roth, Stefan
CY - Cham
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -