Learning and Inference in Parametric Switching Linear Dynamic Systems

Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank

Title:

Learning and Inference in Parametric Switching Linear Dynamic Systems

dc.contributor.author	Oh, Sang Min
dc.contributor.author	Rehg, James M.
dc.contributor.author	Balch, Tucker
dc.contributor.author	Dellaert, Frank
dc.contributor.corporatename	Georgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatename	Georgia Institute of Technology. College of Computing
dc.date.accessioned	2011-04-11T21:39:20Z
dc.date.available	2011-04-11T21:39:20Z
dc.date.issued	2005-10
dc.description	©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.	en_US
dc.description	Presented at the 2005 10th IEEE International Conference on Computer Vision (ICCV), 17-21 October 2005, Beijing, China.
dc.description	DOI: 10.1109/ICCV.2005.135
dc.description.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.	en_US
dc.identifier.citation	Oh, S.M., Rehg, J.M., Balch, T., & Dellaert, F. (2005). “Learning and Inference in Parametric Switching Linear Dynamic Systems”. Proceedings of the 2005 10th IEEE International Conference on Computer Vision (ICCV), 17-21 October 2005, Vol. 2, 1161-1168.	en_US
dc.identifier.issn	1550-5499
dc.identifier.uri	http://hdl.handle.net/1853/38472
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.subject	Expectation-maximization	en_US
dc.subject	Honeybee dance	en_US
dc.subject	Inference	en_US
dc.subject	Learning	en_US
dc.subject	Systematic spatial variations	en_US
dc.subject	Switching linear dynamic systems	en_US
dc.subject	Systematic temporal variations	en_US
dc.title	Learning and Inference in Parametric Switching Linear Dynamic Systems	en_US
dc.type	Text
dc.type.genre	Post-print
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Rehg, James M.
local.contributor.author	Dellaert, Frank
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
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