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

arXiv: https://arxiv.org/abs/2207.06156 · HTML: https://mlsanity.com/paper/2207.06156/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [Generalized optimal sub-pattern assignment metric](https://mlsanity.com/paper/1601.05585/) (2016)
- [Poisson multi-Bernoulli mixture filter: direct derivation and implementation](https://mlsanity.com/paper/1703.04264/) (2017)
- [Multiple target tracking based on sets of trajectories](https://mlsanity.com/paper/1605.08163/) (2016)
- [Trajectory Poisson multi-Bernoulli filters](https://mlsanity.com/paper/2003.12767/) (2020)
- [Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories](https://mlsanity.com/paper/1812.05131/) (2018)
- [Gaussian implementation of the multi-Bernoulli mixture filter](https://mlsanity.com/paper/1908.08819/) (2019)
- [Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering](https://mlsanity.com/paper/1611.06011/) (2016)
- [Tracking Cells and their Lineages via Labeled Random Finite Sets](https://mlsanity.com/paper/2104.10964/) (2021)
- [Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking](https://mlsanity.com/paper/2109.04355/) (2021)
- [Robust Distributed Fusion with Labeled Random Finite Sets](https://mlsanity.com/paper/1710.00501/) (2017)
- [Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter](https://mlsanity.com/paper/1912.01748/) (2019)
- [Poisson Multi-Bernoulli Mapping Using Gibbs Sampling](https://mlsanity.com/paper/1811.03154/) (2018)
- [Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter](https://mlsanity.com/paper/2008.00413/) (2020)
- [Distributed Multi-object Tracking under Limited Field of View Sensors](https://mlsanity.com/paper/2012.12990/) (2020)

## 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.