Sub-sample swapping for sequential Monte Carlo approximation of high-dimensional densities in the context of complex object tracking
Résumé
In this paper, we address the problem of complex object tracking using the par- ticle lter framework, which essentially amounts to estimate high-dimensional distributions by a sequential Monte Carlo algorithm. For this purpose, we rst exploit Dynamic Bayesian Networks to determine conditionally independent subspaces of the object's state space, which allows us to independently perform the particle lter's propagations and corrections over small spaces. Second, we propose a swapping process to transform the weighted particle set provided by the update step of the particle lter into a \new particle set" better focus- ing on high peaks of the posterior distribution. This new methodology, called Swapping-Based Partitioned Sampling, is proved to be mathematically sound and is successfully tested and validated on synthetic video sequences for single or multiple articulated object tracking.
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