Particle filter

Known as: Particle filters, Sequential importance resampling, Sequential Monte Carlo methods 
Particle filters or Sequential Monte Carlo (SMC) methods are a set of genetic-type particle Monte Carlo methodologies to solve the filtering problem… (More)
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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… (More)
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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… (More)
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Highly Cited
2004
Highly Cited
2004
The Kalman filter provides an effective solution to the linear-Gaussian fil tering problem. However, where there is nonlinearity… (More)
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Highly Cited
2004
Highly Cited
2004
We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and… (More)
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Highly Cited
2003
Highly Cited
2003
Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non… (More)
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Highly Cited
2003
Highly Cited
2003
Sequential Bayesian estimation fornonlinear dynamic state-space models involves recursive estimation of filtering and predictive… (More)
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Highly Cited
2002
Highly Cited
2002
A framework for positioning, navigation and tracking problems using particle filters (recursive Monte Carlo methods) is developed… (More)
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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… (More)
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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… (More)
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Highly Cited
1997
Highly Cited
1997
This paper analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not… (More)
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