Details
| Original language | English |
|---|---|
| Pages (from-to) | 75-92 |
| Number of pages | 18 |
| Journal | International Journal of Computer Vision |
| Volume | 87 |
| Issue number | 1-2 |
| Publication status | Published - 15 Nov 2008 |
| Externally published | Yes |
Abstract
Local optimization and filtering have been widely applied to model-based 3D human motion capture. Global stochastic optimization has recently been proposed as promising alternative solution for tracking and initialization. In order to benefit from optimization and filtering, we introduce a multi-layer framework that combines stochastic optimization, filtering, and local optimization. While the first layer relies on interacting simulated annealing and some weak prior information on physical constraints, the second layer refines the estimates by filtering and local optimization such that the accuracy is increased and ambiguities are resolved over time without imposing restrictions on the dynamics. In our experimental evaluation, we demonstrate the significant improvements of the multi-layer framework and provide quantitative 3D pose tracking results for the complete HumanEva-II dataset. The paper further comprises a comparison of global stochastic optimization with particle filtering, annealed particle filtering, and local optimization.
Keywords
- Filtering, Human motion capture, Stochastic optimization, Tracking
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Artificial Intelligence
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In: International Journal of Computer Vision, Vol. 87, No. 1-2, 15.11.2008, p. 75-92.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Optimization and filtering for human motion capture
T2 - AAA multi-layer framework
AU - Gall, Juergen
AU - Rosenhahn, Bodo
AU - Brox, Thomas
AU - Seidel, Hans Peter
PY - 2008/11/15
Y1 - 2008/11/15
N2 - Local optimization and filtering have been widely applied to model-based 3D human motion capture. Global stochastic optimization has recently been proposed as promising alternative solution for tracking and initialization. In order to benefit from optimization and filtering, we introduce a multi-layer framework that combines stochastic optimization, filtering, and local optimization. While the first layer relies on interacting simulated annealing and some weak prior information on physical constraints, the second layer refines the estimates by filtering and local optimization such that the accuracy is increased and ambiguities are resolved over time without imposing restrictions on the dynamics. In our experimental evaluation, we demonstrate the significant improvements of the multi-layer framework and provide quantitative 3D pose tracking results for the complete HumanEva-II dataset. The paper further comprises a comparison of global stochastic optimization with particle filtering, annealed particle filtering, and local optimization.
AB - Local optimization and filtering have been widely applied to model-based 3D human motion capture. Global stochastic optimization has recently been proposed as promising alternative solution for tracking and initialization. In order to benefit from optimization and filtering, we introduce a multi-layer framework that combines stochastic optimization, filtering, and local optimization. While the first layer relies on interacting simulated annealing and some weak prior information on physical constraints, the second layer refines the estimates by filtering and local optimization such that the accuracy is increased and ambiguities are resolved over time without imposing restrictions on the dynamics. In our experimental evaluation, we demonstrate the significant improvements of the multi-layer framework and provide quantitative 3D pose tracking results for the complete HumanEva-II dataset. The paper further comprises a comparison of global stochastic optimization with particle filtering, annealed particle filtering, and local optimization.
KW - Filtering
KW - Human motion capture
KW - Stochastic optimization
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=75149167102&partnerID=8YFLogxK
U2 - 10.1007/s11263-008-0173-1
DO - 10.1007/s11263-008-0173-1
M3 - Article
AN - SCOPUS:75149167102
VL - 87
SP - 75
EP - 92
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
SN - 0920-5691
IS - 1-2
ER -