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SAC
2008
ACM
13 years 4 months ago
Adaptive methods for sequential importance sampling with application to state space models
Abstract. In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms--also known as particle filters--relying on new criteria evaluating the qu...
Julien Cornebise, Eric Moulines, Jimmy Olsson
TCSV
2008
115views more  TCSV 2008»
13 years 4 months ago
Dynamic Proposal Variance and Optimal Particle Allocation in Particle Filtering for Video Tracking
Abstract--This paper presents a novel particle allocation approach to particle filtering which minimizes the total tracking distortion for a fixed number of particles over a video ...
Pan Pan, Dan Schonfeld
AROBOTS
2007
153views more  AROBOTS 2007»
13 years 4 months ago
An integrated particle filter and potential field method applied to cooperative multi-robot target tracking
We describe a novel method whereby a particle filter is used to create a potential field for robot control without prior clustering. We show an application of this technique to ...
Roozbeh Mottaghi, Richard T. Vaughan
ICIP
2005
IEEE
14 years 6 months ago
Augmented particle filtering for efficient visual tracking
Visual tracking is one of the key tasks in computer vision. The particle filter algorithm has been extensively used to tackle this problem due to its flexibility. However the conv...
Chunhua Shen, Michael J. Brooks, Anton van den Hen...
ICPR
2004
IEEE
14 years 5 months ago
Switching Particle Filters for Efficient Real-time Visual Tracking
Particle filtering is an approach to Bayesian estimation of intractable posterior distributions from time series signals distributed by non-Gaussian noise. A couple of variant par...
Kenji Doya, Shin Ishii, Takashi Bando, Tomohiro Sh...