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ICMLA
2007

Uncertainty optimization for robust dynamic optical flow estimation

8 years 4 months ago
Uncertainty optimization for robust dynamic optical flow estimation
We develop an optical flow estimation framework that focuses on motion estimation over time formulated in a Dynamic Bayesian Network. It realizes a spatiotemporal integration of motion information using a dynamic and robust prior that incorporates spatial and temporal coherence constraints on the flow field. The main contribution is the embedding of these particular assumptions on optical flow evolution into the Bayesian propagation approach that leads to a computationally feasible Two-Filter inference method and is applicable for on- and offline parameter optimization. We analyse the possibility to optimize imposed Student’s t-distributed model uncertainties, which are the camera noise and the transition noise. Experiments with synthetic sequences illustrate how the probabilistic framework improves the optical flow estimation because it allows for noisy data, motion ambiguities and motion discontinuities.
Volker Willert, Marc Toussaint, Julian Eggert, Edg
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ICMLA
Authors Volker Willert, Marc Toussaint, Julian Eggert, Edgar Körner
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