Details
| Original language | English |
|---|---|
| Pages (from-to) | 27-34 |
| Number of pages | 8 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 1998 |
| Early online date | 1 Jan 2001 |
| Publication status | Published - 12 Jun 2001 |
Abstract
This contribution describes a camera-based approach to fully automatically extract the 3D motion parameters of persons using a model based strategy. In a first step a 3D body model of the person to be tracked is constructed automatically using a calibrated setup of sixteen digital cameras and a monochromatic background. From the silhouette images the 3D shape of the person is determined using the shape-from-silhouette approach. This model is segmented into rigid body parts and a dynamic skeleton structure is fit. In the second step the resulting movable, personalized body template is exploited to estimate the 3D motion parameters of the person in arbitrary poses. Using the same camera setup and the shape-from-silhouette approach a sequence of volume data is captured to which the movable body template is fit. Using a modified ICP algorithm the fitting is performed in a hierarchical manner along the kinematic chains of the body model. The resulting sequence of motion parameters for the articulated body model can be used for gesture recognition, control of virtual characters or robot manipulators.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 1998, 12.06.2001, p. 27-34.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Hierarchical 3D pose estimation for articulated human body models from a sequence of volume data
AU - Weik, Sebastian
AU - Liedtke, C. E.
PY - 2001/6/12
Y1 - 2001/6/12
N2 - This contribution describes a camera-based approach to fully automatically extract the 3D motion parameters of persons using a model based strategy. In a first step a 3D body model of the person to be tracked is constructed automatically using a calibrated setup of sixteen digital cameras and a monochromatic background. From the silhouette images the 3D shape of the person is determined using the shape-from-silhouette approach. This model is segmented into rigid body parts and a dynamic skeleton structure is fit. In the second step the resulting movable, personalized body template is exploited to estimate the 3D motion parameters of the person in arbitrary poses. Using the same camera setup and the shape-from-silhouette approach a sequence of volume data is captured to which the movable body template is fit. Using a modified ICP algorithm the fitting is performed in a hierarchical manner along the kinematic chains of the body model. The resulting sequence of motion parameters for the articulated body model can be used for gesture recognition, control of virtual characters or robot manipulators.
AB - This contribution describes a camera-based approach to fully automatically extract the 3D motion parameters of persons using a model based strategy. In a first step a 3D body model of the person to be tracked is constructed automatically using a calibrated setup of sixteen digital cameras and a monochromatic background. From the silhouette images the 3D shape of the person is determined using the shape-from-silhouette approach. This model is segmented into rigid body parts and a dynamic skeleton structure is fit. In the second step the resulting movable, personalized body template is exploited to estimate the 3D motion parameters of the person in arbitrary poses. Using the same camera setup and the shape-from-silhouette approach a sequence of volume data is captured to which the movable body template is fit. Using a modified ICP algorithm the fitting is performed in a hierarchical manner along the kinematic chains of the body model. The resulting sequence of motion parameters for the articulated body model can be used for gesture recognition, control of virtual characters or robot manipulators.
UR - http://www.scopus.com/inward/record.url?scp=84945124216&partnerID=8YFLogxK
U2 - 10.1007/3-540-44690-7_4
DO - 10.1007/3-540-44690-7_4
M3 - Article
AN - SCOPUS:84945124216
VL - 1998
SP - 27
EP - 34
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SN - 0302-9743
ER -