Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1428-1435 |
| Seitenumfang | 8 |
| Fachzeitschrift | Proceedings of the IEEE International Conference on Computer Vision |
| Publikationsstatus | Veröffentlicht - 2009 |
| Veranstaltung | 12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan Dauer: 29 Sept. 2009 → 2 Okt. 2009 |
Abstract
In this paper, we introduce a novel iterative motion tracking framework that combines 3D tracking techniques with motion retrieval for stabilizing markerless human motion capturing. The basic idea is to start human tracking without prior knowledge about the performed actions. The resulting 3D motion sequences, which may be corrupted due to tracking errors, are locally classified according to available motion categories. Depending on the classification result, a retrieval system supplies suitable motion priors, which are then used to regularize and stabilize the tracking in the next iteration step. Experiments with the HumanEVA-II benchmark show that tracking and classification are remarkably improved after few iterations.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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in: Proceedings of the IEEE International Conference on Computer Vision, 2009, S. 1428-1435.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Stabilizing Motion Tracking Using Retrieved Motion Priors
AU - Baak, Andreas
AU - Rosenhahn, Bodo
AU - Müller, Meinard
AU - Seidel, Hans Peter
N1 - Funding Information: Acknowledgments. The first and second author are funded by the German Research Foundation (DFG CL 64/5-1, RO 2497/6-1), the third author by the Cluster of Excellence on Multimodal Computing and Interaction.
PY - 2009
Y1 - 2009
N2 - In this paper, we introduce a novel iterative motion tracking framework that combines 3D tracking techniques with motion retrieval for stabilizing markerless human motion capturing. The basic idea is to start human tracking without prior knowledge about the performed actions. The resulting 3D motion sequences, which may be corrupted due to tracking errors, are locally classified according to available motion categories. Depending on the classification result, a retrieval system supplies suitable motion priors, which are then used to regularize and stabilize the tracking in the next iteration step. Experiments with the HumanEVA-II benchmark show that tracking and classification are remarkably improved after few iterations.
AB - In this paper, we introduce a novel iterative motion tracking framework that combines 3D tracking techniques with motion retrieval for stabilizing markerless human motion capturing. The basic idea is to start human tracking without prior knowledge about the performed actions. The resulting 3D motion sequences, which may be corrupted due to tracking errors, are locally classified according to available motion categories. Depending on the classification result, a retrieval system supplies suitable motion priors, which are then used to regularize and stabilize the tracking in the next iteration step. Experiments with the HumanEVA-II benchmark show that tracking and classification are remarkably improved after few iterations.
UR - http://www.scopus.com/inward/record.url?scp=84885018308&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2009.5459291
DO - 10.1109/ICCV.2009.5459291
M3 - Conference article
AN - SCOPUS:84885018308
SP - 1428
EP - 1435
JO - Proceedings of the IEEE International Conference on Computer Vision
JF - Proceedings of the IEEE International Conference on Computer Vision
SN - 1550-5499
T2 - 12th International Conference on Computer Vision, ICCV 2009
Y2 - 29 September 2009 through 2 October 2009
ER -