Title:
Learning and Inferring Motion Patterns Using Parametric Segmental Switching Linear Dynamic Systems
Learning and Inferring Motion Patterns Using Parametric Segmental Switching Linear Dynamic Systems
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Author(s)
Oh, Sang Min
Rehg, James M.
Balch, Tucker
Dellaert, Frank
Rehg, James M.
Balch, Tucker
Dellaert, Frank
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Abstract
Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic
systems. An SLDS provides the possibility to describe complex temporal patterns more concisely and accurately than an
HMM by using continuous hidden states. However, the use of SLDS models in practical applications is challenging for
several reasons. First, exact inference in SLDS models is computationally intractable. Second, the geometric duration model
induced in standard SLDSs limits their representational power. Third, standard SLDSs do not provide a systematic way to
robustly interpret systematic variations governed by higher order parameters.
The contributions in this paper address all three challenges above. First, we present a data-driven MCMC sampling
method for SLDSs as a robust and efficient approximate inference method. Second, we present segmental switching linear
dynamic systems (S-SLDS), where the geometric distributions are replaced with arbitrary duration models. Third, we extend
the standard model with a parametric model that can capture systematic temporal and spatial variations. The resulting
parametric SLDS model (P-SLDS) uses EM to robustly interpret parametrized motions by incorporating additional global
parameters that underly systematic variations of the overall motion.
The overall development of the proposed inference methods and extensions for SLDSs provide a robust framework to
interpret complex motions. The framework is applied to the honey bee dance interpretation task in the context of the on-going
BioTracking project at Georgia Institute of Technology. The experimental results suggest that the enhanced models provide
an effective framework for a wide range of motion analysis applications.
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Date Issued
2008
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