Evolutionary resampling for multi-target tracking using probability hypothesis density filter

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Details zur Publikation

Autor(en): Halimeh MM, Brendel A, Kellermann W
Jahr der Veröffentlichung: 2018
Seitenbereich: 647-651
ISBN: 978-90-827970-1-5
Sprache: Englisch


Abstract

A resampling scheme is proposed for use with
Sequential Monte Carlo (SMC)-based Probability Hypothesis Density(PHD)
filters. It consists of two steps, first, regions of interest are
identified, then an evolutionary resampling is applied for each region.
Applying resampling locally corresponds to treating each target
individually, while the evolutionary resampling introduces a memory to a
group of particles, increasing the robustness of the estimation against
noise outliers. The proposed approach is compared to the original
SMC-PHD filter for tracking multiple targets in a deterministically
moving targets scenario, and a noisy motion scenario. In both cases, the
proposed approach provides more accurate estimates.


FAU-Autoren / FAU-Herausgeber

Brendel, Andreas
Professur für Nachrichtentechnik
Halimeh, MHD Modar
Lehrstuhl für Digitale Übertragung
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


Zitierweisen

APA:
Halimeh, M.M., Brendel, A., & Kellermann, W. (2018). Evolutionary resampling for multi-target tracking using probability hypothesis density filter. (pp. 647-651). Rome, IT.

MLA:
Halimeh, MHD Modar, Andreas Brendel, and Walter Kellermann. "Evolutionary resampling for multi-target tracking using probability hypothesis density filter." Proceedings of the European Signal Processing Conference (EUSIPCO), Rome 2018. 647-651.

BibTeX: 

Zuletzt aktualisiert 2018-16-10 um 11:23