Title:
Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model
Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model
Author(s)
Balch, Tucker
Dellaert, Frank
Khan, Zia
Dellaert, Frank
Khan, Zia
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Abstract
We describe a multiple hypothesis particle filter
for tracking targets that will be influenced by the proximity
and/or behavior of other targets. Our contribution is to show
how a Markov random field motion prior, built on the fly
at each time step, can model these interactions to enable
more accurate tracking. We present results for a social insect
tracking application, where we model the domain knowledge
that two targets cannot occupy the same space, and targets
will actively avoid collisions. We show that using this model
improves track quality and efficiency. Unfortunately, the
joint particle tracker we propose suffers from exponential
complexity in the number of tracked targets. An approximation
to the joint filter, however, consisting of multiple
nearly independent particle filters can provide similar track
quality at substantially lower computational cost.
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Date Issued
2003
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Paper