ausblenden:
Schlagwörter:
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Zusammenfassung:
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 \texttt{HumanEva-II}
dataset.
The paper further comprises a comparison of global stochastic optimization
with particle filtering, annealed particle filtering, and local optimization.
