Graphical stochastic models for tracking applications with variational message passing inference

F. Trusheim; A. Condurache; A. Mertins

doi:10.1109/IPTA.2016.7820985

Graphical stochastic models for tracking applications with variational message passing inference

F. Trusheim, A. Condurache, A. Mertins

Institute of Signal Processing

Robert Bosch GmbH

2 Citations (Scopus)

Abstract

In this paper we present a novel, highly-adoptable, state-estimation filter based on the framework of graphical stochastical models and variational message passing inference. We evaluate our method on both real and simulated data for tracking applications. Our experimental results show that the proposed approach offers qualitative and computational advantages over established filter methods in practical situations, where the noise within a process is not simply a Gaussian noise, but rather described by a more complex distribution.

Original language	English
Title of host publication	2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Number of pages	6
Publisher	IEEE
Publication date	01.12.2016
Pages	1-6
Article number	7820985
ISBN (Print)	978-1-4673-8911-2
ISBN (Electronic)	978-1-4673-8910-5
DOIs	https://doi.org/10.1109/IPTA.2016.7820985
Publication status	Published - 01.12.2016
Event	6th International Conference on Image Processing Theory, Tools and Applications - Oulu, Finland Duration: 12.12.2016 → 15.12.2016 Conference number: 125997

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title = "Graphical stochastic models for tracking applications with variational message passing inference",

abstract = "In this paper we present a novel, highly-adoptable, state-estimation filter based on the framework of graphical stochastical models and variational message passing inference. We evaluate our method on both real and simulated data for tracking applications. Our experimental results show that the proposed approach offers qualitative and computational advantages over established filter methods in practical situations, where the noise within a process is not simply a Gaussian noise, but rather described by a more complex distribution.",

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Trusheim, F, Condurache, A & Mertins, A 2016, Graphical stochastic models for tracking applications with variational message passing inference. in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)., 7820985, IEEE Proceedings, IEEE, pp. 1-6, 6th International Conference on Image Processing Theory, Tools and Applications, Oulu, Finland, 12.12.16. https://doi.org/10.1109/IPTA.2016.7820985

Graphical stochastic models for tracking applications with variational message passing inference. / Trusheim, F.; Condurache, A.; Mertins, A.
2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. p. 1-6 7820985 (IEEE Proceedings).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

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BT - 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)

PB - IEEE

T2 - 6th International Conference on Image Processing Theory, Tools and Applications

Y2 - 12 December 2016 through 15 December 2016

ER -

Graphical stochastic models for tracking applications with variational message passing inference

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