A Linear-Complexity Second-Order Multi-Object Filter via Factorial Cumulants

Daniel E. Clark, Flávio De Melo

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

3 Citations (Scopus)

Abstract

Multi-target tracking solutions with low computational complexity are required in order to address large-scale tracking problems. Solutions based on statistics determined from point processes, such as the PHD filter, CPHD filter, and newer second-order PHD filter are some examples of these algorithms. There are few solutions of linear complexity in the number of targets and number of measurements, with the PHD filter being one exception. However, the trade-off is that it is unable to propagate beyond first-order moment statistics. In this paper, a new filter is proposed with the same complexity as the PHD filter that also propagates second-order information via the second-order factorial cumulant. The results show that the algorithm is more robust than the PHD filter in challenging clutter environments.

Original languageEnglish
Title of host publication2018 21st International Conference on Information Fusion (FUSION)
PublisherIEEE
Pages1250-1259
Number of pages10
ISBN (Electronic)9780996452762
DOIs
Publication statusPublished - 6 Sep 2018
Event21st International Conference on Information Fusion 2018 - Cambridge, United Kingdom
Duration: 10 Jul 201813 Jul 2018

Conference

Conference21st International Conference on Information Fusion 2018
Abbreviated titleFUSION 2018
CountryUnited Kingdom
CityCambridge
Period10/07/1813/07/18

Keywords

  • factorial cumulants
  • Multi-target tracking
  • point processes

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Statistics, Probability and Uncertainty
  • Instrumentation

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