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

Halimeh MM, Brendel A, Kellermann W (2018)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2018

Pages Range: 647-651

Event location: Rome IT

ISBN: 978-90-827970-1-5

DOI: 10.23919/eusipco.2018.8553478

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.

Authors with CRIS profile

Mhd Modar Halimeh Professur für Signalverarbeitung Andreas Brendel Professur für Signalverarbeitung Walter Kellermann Professur für Signalverarbeitung

How to cite

APA:

Halimeh, M.M., Brendel, A., & Kellermann, W. (2018). Evolutionary resampling for multi-target tracking using probability hypothesis density filter. In Proceedings of the European Signal Processing Conference (EUSIPCO) (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.

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