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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Related topics

Related topics

49 relations
Artificial intelligenceAugmented realityAuxiliary particle filterBioinformatics
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Broader (4)

Computational statisticsControl theoryEstimation theoryRobot control

Papers overview

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Highly Cited
2007
Highly Cited
2007
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
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
A Boosted Particle Filter: Multitarget Detection and Tracking
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
An Improved Particle Filter for Non-linear Problems
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
Visual tracking and recognition using appearance-adaptive models in particle filters
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
An adaptive color-based particle filter
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
Gaussian particle filtering
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
Particle filters for positioning, navigation, and tracking
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
The Unscented Particle Filter
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
Articulated Body Motion Capture by Annealed Particle Filtering
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
FILTERING VIA SIMULATION : AUXILIARY PARTICLE FILTERS
This paper analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not… (More)
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