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Dellaert, Frank

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Author: Dellaert, Frank × Author: Oh, Sang Min × End date: 2006 × Start date: 2006 ×

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Parameterized Duration Modeling for Switching Linear Dynamic Systems

2006-06 , Oh, Sang Min , Rehg, James M. , Dellaert, Frank

We introduce an extension of switching linear dynamic systems (SLDS) with parameterized duration modeling capabilities. The proposed model allows arbitrary duration models and overcomes the limitation of a geometric distribution induced in standard SLDSs. By incorporating a duration model which reflects the data more closely, the resulting model provides reliable inference results which are robust against observation noise. Moreover, existing inference algorithms for SLDSs can be adopted with only modest additional effort in most cases where an SLDS model can be applied. In addition, we observe the fact that the duration models would vary across data sequences in certain domains, which complicates learning and inference tasks. Such variability in duration is overcome by introducing parameterized duration models. The experimental results on honeybee dance decoding tasks demonstrate the robust inference capabilities of the proposed model.

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Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems

2006 , 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 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 ongoing 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.

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On-line Learning of the Traversability of Unstructured Terrain for Outdoor Robot Navigation

2006 , Oh, Sang Min , Rehg, James M. , Dellaert, Frank

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Automatic Acquisition of 4D urban Models and Proactive Auditory Service for Enhanced User Experience

2006 , Oh, Sang Min , Schildler, Grant , Dellaert, Frank

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