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A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth

arXiv 2207.06156 ↗First posted 13 Jul 2022stat.AP cs.CV cs.SYcited by 0

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Abstract (arXiv)

This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models. The PMBM track initiation is obtained via Bayes' rule applied on the predicted PMBM density, and creates one Bernoulli component for each received measurement, representing that this measurement may be clutter or a detection from a new target. Adaptive birth mimics this procedure by creating a Bernoulli component for each measurement using a different rule to determine the probability of existence and a user-defined single-target density. This paper first provides an analysis of the differences that arise in track initiation based on isolated measurements. Then, it shows that adaptive birth underestimates the number of objects present in the surveillance area under common modelling assumptions. Finally, we provide numerical simulations to further illustrate the differences.

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References · 14

  1. 2016Generalized optimal sub-pattern assignment metriccited by 33
  2. 2017Poisson multi-Bernoulli mixture filter: direct derivation and implementationcited by 29
  3. 2016Multiple target tracking based on sets of trajectoriescited by 11
  4. 2020Trajectory Poisson multi-Bernoulli filterscited by 11
  5. 2018Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectoriescited by 10
  6. 2019Gaussian implementation of the multi-Bernoulli mixture filtercited by 7
  7. 2016Online Visual Multi-Object Tracking via Labeled Random Finite Set Filteringcited by 6
  8. 2021Tracking Cells and their Lineages via Labeled Random Finite Setscited by 5
  9. 2021Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Trackingcited by 5
  10. 2017Robust Distributed Fusion with Labeled Random Finite Setscited by 4
  11. 2019Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filtercited by 4
  12. 2018Poisson Multi-Bernoulli Mapping Using Gibbs Samplingcited by 1
2 more
  1. 2020Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filtercited by 1
  2. 2020Distributed Multi-object Tracking under Limited Field of View Sensorscited by 1

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