Evolutionary Particle Filters: Model-free Object Tracking - Combining Evolution Strategies and Particle Filters

Silja Meyer-Nieberg, Erik Kropat, Stefan Pickl

Abstract

Tracking situations or more generally state estimation of dynamic systems arise in various application contexts. Usually the state-evolution equations are assumed to be known up to certain parameters. But what can be done if this is not the case? This paper presents an innovative approach to solve this difficult and complex situation by using the inherent tracking abilities of evolution strategies. Combining principles of particle filters and evolution strategies leads to a new type of algorithms: evolutionary particle filters. Their tracking quality is examined in simulations.

References

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Paper Citation


in Harvard Style

Meyer-Nieberg S., Kropat E. and Pickl S. (2013). Evolutionary Particle Filters: Model-free Object Tracking - Combining Evolution Strategies and Particle Filters . In Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-8565-40-2, pages 96-102. DOI: 10.5220/0004284300960102


in Bibtex Style

@conference{icores13,
author={Silja Meyer-Nieberg and Erik Kropat and Stefan Pickl},
title={Evolutionary Particle Filters: Model-free Object Tracking - Combining Evolution Strategies and Particle Filters},
booktitle={Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2013},
pages={96-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004284300960102},
isbn={978-989-8565-40-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Evolutionary Particle Filters: Model-free Object Tracking - Combining Evolution Strategies and Particle Filters
SN - 978-989-8565-40-2
AU - Meyer-Nieberg S.
AU - Kropat E.
AU - Pickl S.
PY - 2013
SP - 96
EP - 102
DO - 10.5220/0004284300960102