Title:
A Rao-Blackwellized Particle Filter for EigenTracking
A Rao-Blackwellized Particle Filter for EigenTracking
Author(s)
Khan, Zia
Balch, Tucker
Dellaert, Frank
Balch, Tucker
Dellaert, Frank
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
Subspace representations have been a popular way to
model appearance in computer vision. In Jepson and
Black’s influential paper on EigenTracking, they were
successfully applied in tracking. For noisy targets,
optimization-based algorithms (including EigenTracking)
often fail catastrophically after losing track. Particle
filters have recently emerged as a robust method
for tracking in the presence of multi-modal distributions.
To use subspace representations in a particle filter, the
number of samples increases exponentially as the state
vector includes the subspace coefficients. We introduce
an efficient method for using subspace representations
in a particle filter by applying Rao-Blackwellization to
integrate out the subspace coefficients in the state vector.
Fewer samples are needed since part of the posterior
over the state vector is analytically calculated. We
use probabilistic principal component analysis to obtain
analytically tractable integrals. We show experimental
results in a scenario in which we track a target in clutter.
Sponsor
Date Issued
2004-06
Extent
Resource Type
Text
Resource Subtype
Post-print
Proceedings
Proceedings