(Georgia Institute of Technology, 2005-10)
Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank
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.
(Georgia Institute of Technology, 2005-07)
Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank
Switching Linear Dynamic System (SLDS) models are
a popular technique for modeling complex nonlinear dynamic
systems. An SLDS has significantly more descriptive
power than an HMM, but inference in SLDS models is
computationally intractable. This paper describes a novel
inference algorithm for SLDS models based on the Data-
Driven MCMC paradigm. We describe a new proposal
distribution which substantially increases the convergence
speed. Comparisons to standard deterministic approximation
methods demonstrate the improved accuracy of our new
approach. We apply our approach to the problem of learning
an SLDS model of the bee dance. Honeybees communicate
the location and distance to food sources through a
dance that takes place within the hive. We learn SLDS
model parameters from tracking data which is automatically
extracted from video. We then demonstrate the ability to
successfully segment novel bee dances into their constituent
parts, effectively decoding the dance of the bees.