Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes
This addresses the problem of efficient and accurate multi-object tracking in sensor networks, offering a decentralised solution that matches centralised performance, though it is incremental as it builds on existing Variational Inference and optimisation techniques.
The paper tackles multi-sensor multi-object tracking by proposing decentralised Variational Inference schemes that achieve tracking performance equivalent to centralised fusion with only local message exchanges, notably with the decentralised natural gradient VI method being the most communication-efficient while delivering higher tracking accuracy.
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.