Title:
MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements
MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements
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Author(s)
Khan, Zia
Balch, Tucker
Dellaert, Frank
Balch, Tucker
Dellaert, Frank
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Abstract
In several multitarget tracking applications, a target may return more than one measurement per target and interacting
targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially
applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for
interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in
this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to
eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares
updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when
combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include
experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video,
and laser range data. We also show the algorithm exhibits real time performance on a conventional PC.
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Date Issued
2006-12
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Text
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Article