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Highly Cited

2007

Highly Cited

2007

Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous… Expand

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Highly Cited

2005

Highly Cited

2005

This contribution is devoted to the comparison of various resampling approaches that have been proposed in the literature on… Expand

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Highly Cited

2004

Highly Cited

2004

The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and… Expand

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Highly Cited

2003

Highly Cited

2003

Abstract Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for… Expand

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Highly Cited

2003

Highly Cited

2003

Sequential Bayesian estimation for nonlinear dynamic state-space models involves recursive estimation of filtering and predictive… Expand

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Highly Cited

2002

Highly Cited

2002

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is… Expand

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Highly Cited

2000

Highly Cited

2000

In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented… Expand

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Highly Cited

2000

Highly Cited

2000

Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow… Expand

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Highly Cited

2000

Highly Cited

2000

The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered… Expand

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Highly Cited

1999

Highly Cited

1999

This article analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not… Expand

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