Joint multi-object and clutter rate estimation with the single-cluster PHD filter

Isabel Schlangen, Vibhav Bharti, Emmanuel Delande, Daniel E. Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

When working with real data, underlying parameters such as the detection or clutter rates are generally unknown and possibly varying over time, however the right parametrisation is crucial to extract proper statistics about the monitored objects. In this article, a single cluster Probability Hypothesis Density (PHD) filter is used to jointly estimate the location and number of a set of objects and the clutter rate over time. The algorithm is verified on a simulated scenario designed to emulate the challenging nature of Single-Molecule Localisation Microscopy (SMLM) imaging sequences and demonstrated on a similar scenario with real data.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE
Pages1087-1091
Number of pages5
ISBN (Electronic)9781509011711
DOIs
Publication statusPublished - 19 Jun 2017
Event14th IEEE International Symposium on Biomedical Imaging 2017 - Melbourne, Australia
Duration: 18 Apr 201721 Apr 2017

Conference

Conference14th IEEE International Symposium on Biomedical Imaging 2017
Abbreviated titleISBI 2017
CountryAustralia
CityMelbourne
Period18/04/1721/04/17

Keywords

  • Clutter estimation
  • Multi-target tracking
  • Single-cluster PHD filter
  • Single-molecule localisation microscopy

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Joint multi-object and clutter rate estimation with the single-cluster PHD filter'. Together they form a unique fingerprint.

  • Cite this

    Schlangen, I., Bharti, V., Delande, E., & Clark, D. E. (2017). Joint multi-object and clutter rate estimation with the single-cluster PHD filter. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 1087-1091). [7950705] IEEE. https://doi.org/10.1109/ISBI.2017.7950705