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

TL;DR

This paper introduces decentralised Variational Inference schemes for multi-object tracking in sensor networks, achieving performance comparable to centralised methods with reduced communication and faster convergence.

Contribution

It proposes a novel decentralised gradient-based VI framework optimizing LM-ELBO, improving convergence speed and communication efficiency in multi-sensor tracking.

Findings

Decentralised VI schemes match centralised tracking performance.

Natural gradient VI significantly reduces communication costs.

Enhanced convergence speed with gradient tracking strategies.

Abstract

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.