.png){kind=icon}
Return
Optimization and Filtering for Human Motion Capture
.png){kind=icon}
OA
AI
DOI：10.1007/s11263-008-0173-1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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:
Human motion capture
Stochastic optimization
Filtering
Tracking
Journal
IF:
9.3
Papers:
3.9K
Citations:
2.8W
Organization
Cited Papers
ShareSaveShareSaveShareSave
ShareSaveCarbon nanotube induced double percolation in polymer blends: Morphology, rheology and broadband dielectric properties.png)
Polymer
PolymerIF0
PREAI
ShareSave
View more