By Topic

Learning and inference in parametric switching linear dynamic systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Sang Min Oh ; Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA ; J. M. Rehg ; T. Balch ; F. Dellaert

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 (1999) presented parametric HMMs, 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

Published in:

Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1  (Volume:2 )

Date of Conference:

17-21 Oct. 2005