# Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network

arXiv: https://arxiv.org/abs/2309.00807 · HTML: https://mlsanity.com/paper/2309.00807/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2023.

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

- [Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes](https://mlsanity.com/paper/2408.13689/) (2024)

## Abstract (arXiv)

The growing need for accurate and reliable tracking systems has driven significant progress in sensor fusion and object tracking techniques. In this paper, we design two variational Bayesian trackers that effectively track multiple targets in cluttered environments within a sensor network. We first present a centralised sensor fusion scheme, which involves transmitting sensor data to a fusion center. Then, we develop a distributed version leveraging the average consensus algorithm, which is theoretically equivalent to the centralised sensor fusion tracker and requires only local message passing with neighbouring sensors. In addition, we empirically verify that our proposed distributed variational tracker performs on par with the centralised version with equal tracking accuracy. Simulation results show that our distributed multi-target tracker outperforms the suboptimal distributed sensor fusion strategy that fuses each sensor's posterior based on arithmetic sensor fusion and an average consensus strategy.