# Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes

arXiv: https://arxiv.org/abs/2408.13689 · HTML: https://mlsanity.com/paper/2408.13689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs](https://mlsanity.com/paper/1607.03218/) (2016)
- [Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam](https://mlsanity.com/paper/1806.04854/) (2018)
- [On Nonconvex Decentralized Gradient Descent](https://mlsanity.com/paper/1608.05766/) (2016)
- [Generalized optimal sub-pattern assignment metric](https://mlsanity.com/paper/1601.05585/) (2016)
- [Structured Stochastic Variational Inference](https://mlsanity.com/paper/1404.4114/) (2014)
- [Conditionally conjugate mean-field variational Bayes for logistic models](https://mlsanity.com/paper/1711.06999/) (2017)
- [Poisson multi-Bernoulli conjugate prior for multiple extended object filtering](https://mlsanity.com/paper/1605.06311/) (2016)
- [Distributed Variational Bayesian Algorithms Over Sensor Networks](https://mlsanity.com/paper/2011.13600/) (2020)
- [Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter: Supplementary Materials](https://mlsanity.com/paper/2309.01774/) (2023)
- [Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network](https://mlsanity.com/paper/2309.00807/) (2023)

## Built on by (most cited first, 1 of 1)

- [PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter](https://mlsanity.com/paper/2607.13891/) (2026)

## Abstract (arXiv)

This paper tackles the challenge of multi-sensor multi-object tracking by proposing various decentralised Variational Inference (VI) schemes that match the tracking performance of centralised sensor fusion with only local message exchanges among neighboring sensors. We first establish a centralised VI sensor fusion scheme as a benchmark and analyse the limitations of its decentralised counterpart, which requires sensors to await consensus at each VI iteration. Therefore, we propose a decentralised gradient-based VI framework that optimises the Locally Maximised Evidence Lower Bound (LM-ELBO) instead of the standard ELBO, which reduces the parameter search space and enables faster convergence, making it particularly beneficial for decentralised tracking. This proposed framework is inherently self-evolving, improving with advancements in decentralised optimisation techniques for convergence guarantees and efficiency. Further, we enhance the convergence speed of proposed decentralised schemes using natural gradients and gradient tracking strategies. Results verify that our decentralised VI schemes are empirically equivalent to centralised fusion in tracking performance. Notably, the decentralised natural gradient VI method is the most communication-efficient, with communication costs comparable to suboptimal decentralised strategies while delivering notably higher tracking accuracy.