Graphical stochastic models for tracking applications with variational message passing inference

F. Trusheim, A. Condurache, A. Mertins

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.
OriginalspracheEnglisch
Titel2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Seitenumfang6
Herausgeber (Verlag)IEEE
Erscheinungsdatum01.12.2016
Seiten1-6
Aufsatznummer7820985
ISBN (Print)978-1-4673-8911-2
ISBN (elektronisch)978-1-4673-8910-5
DOIs
PublikationsstatusVeröffentlicht - 01.12.2016
Veranstaltung6th International Conference on Image Processing Theory, Tools and Applications - Oulu, Finnland
Dauer: 12.12.201615.12.2016
Konferenznummer: 125997

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