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dc.contributor.authorOh, Sang Min
dc.contributor.authorRehg, James M.
dc.contributor.authorBalch, Tucker
dc.contributor.authorDellaert, Frank
dc.date.accessioned2011-04-22T00:51:00Z
dc.date.available2011-04-22T00:51:00Z
dc.date.issued2008
dc.identifier.citationOh,S.M., Rehg, J.M.,Balch, T.,& Dellaert, F. (2008). “Learning and Inferring Motion Patterns Using Parametric Segmental Switching Linear Dynamic Systems”. International Journal of Computer Vision (IJCV) Special Issue on Learning for Vision, Vol. 77, no. 1-3, (May 2008), pp. 103-124.en_US
dc.identifier.issn0920-5691
dc.identifier.urihttp://hdl.handle.net/1853/38686
dc.description©2006 Springer-Verlag Berlin Heidelberg. The original publication is available at www.springerlink.comen_US
dc.descriptionDOI: 10.1007/s11263-007-0062-z
dc.description.abstractSwitching 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.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectData-drivenen_US
dc.subjectGlobal parametersen_US
dc.subjectHoneybee danceen_US
dc.subjectInference methodsen_US
dc.subjectProbabilistic inferenceen_US
dc.subjectSwitching linear dynamic systemen_US
dc.subjectTime-series modelingen_US
dc.titleLearning and Inferring Motion Patterns Using Parametric Segmental Switching Linear Dynamic Systemsen_US
dc.typeArticleen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.contributor.corporatenameGeorgia Institute of Technology. Graphics, Visualization and Usability Center
dc.publisher.originalSpringer Berlin / Heidelberg


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