Title:
Learning and Inference in Parametric Switching Linear Dynamic Systems
Learning and Inference in Parametric 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
We introduce parametric switching linear dynamic
systems (P-SLDS) for learning and interpretation of
parametrized motion, i.e., motion that exhibits systematic
temporal and spatial variations. Our motivating example
is the honeybee dance: bees communicate the orientation
and distance to food sources through the dance angles and
waggle lengths of their stylized dances. Switching linear dynamic
systems (SLDS) are a compelling way to model such
complex motions. However, SLDS does not provide a means
to quantify systematic variations in the motion. Previously,
Wilson & Bobick presented parametric HMMs [21], an extension
to HMMs with which they successfully interpreted
human gestures. Inspired by their work, we similarly extend
the standard SLDS model to obtain parametric SLDS.
We introduce additional global parameters that represent
systematic variations in the motion, and present general
expectation-maximization (EM) methods for learning and
inference. In the learning phase, P-SLDS learns canonical
SLDS model from data. In the inference phase, P-SLDS
simultaneously quantifies the global parameters and labels
the data. We apply these methods to the automatic interpretation
of honey-bee dances, and present both qualitative
and quantitative experimental results on actual bee-tracks
collected from noisy video data.
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Date Issued
2005-10
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