Variational Inference and Learning of Piecewise-linear Dynamical Systems

Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many real-world processes that cannot be characterized by a single l...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Alameda-Pineda, Xavier, Drouard, Vincent, Horaud, Radu
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.11.2020
Subjects
Algorithms
Dynamical systems
Mathematical models
Optical tracking
Parameters
Solvers
Switching
Online AccessGet full text

Cover

More Information
Summary:Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many real-world processes that cannot be characterized by a single linear behavior. Alternatively, it is possible to consider a piecewise-linear model which, combined with a switching mechanism, is well suited when several modes of behavior are needed. Nevertheless, switching dynamical systems are intractable because of their computational complexity increases exponentially with time. In this paper, we propose a variational approximation of piecewise linear dynamical systems. We provide full details of the derivation of two variational expectation-maximization algorithms, a filter and a smoother. We show that the model parameters can be split into two sets, static and dynamic parameters, and that the former parameters can be estimated off-line together with the number of linear modes, or the number of states of the switching variable. We apply the proposed method to a visual tracking problem, namely head-pose tracking, and we thoroughly compare our algorithm with several state of the art trackers.
ISSN:2331-8422