A Bayesian Nonparametric Approach to Modeling Motion Patterns
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auro11_tech_report.pdf
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main article
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2.83 MB
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Author(s)
Joseph, Joshua Mason
Doshi-Velez, Finale P.
Huang, Albert S.
Roy, Nicholas
Date Issued
August 2011
Journal
Autonomous Robots
Publisher
Kluwer Academic Publishers
Citation
Joseph, Joshua, et al. "A Bayesian Nonparametric Approach to Modeling Motion Patterns" Autonomous Robots, 2011, 31.4 p.383–400. (From the issue entitled "Special Issue: Search and Pursuit-evasion with Mobile Robots")
Version
Original manuscript
Abstract
The most difficult—and often most essential—
aspect of many interception and tracking tasks is constructing
motion models of the targets to be found. Experts can
often provide only partial information, and fitting parameters
for complex motion patterns can require large amounts
of training data. Specifying how to parameterize complex
motion patterns is in itself a difficult task.
In contrast, nonparametric models are very flexible and
generalize well with relatively little training data. We propose
modeling target motion patterns as a mixture of Gaussian
processes (GP) with a Dirichlet process (DP) prior over
mixture weights. The GP provides a flexible representation
for each individual motion pattern, while the DP assigns observed
trajectories to particular motion patterns. Both automatically
adjust the complexity of the motion model based
on the available data. Our approach outperforms several parametric
models on a helicopter-based car-tracking task on
data collected from the greater Boston area.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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DOI of Published Version
http://dx.doi.org/10.1007/s10514-011-9248-x
https://doi.org/10.1007/s10514-011-9248-x
