Principled information fusion for multi-view multi-agent surveillance systems

TL;DR

This paper introduces a new principled information fusion method based on Generalized Covariance Intersection for multi-view multi-agent surveillance, enabling effective multi-object tracking in centralized and distributed sensor networks.

Contribution

It proposes a novel fusion approach that improves multi-object tracking by effectively combining information from multiple agents using GCI, applicable to both centralized and distributed systems.

Findings

Effective multi-object tracking demonstrated in simulations.

Fusion method outperforms existing approaches.

Applicable to both centralized and peer-to-peer networks.

Abstract

A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multi-view agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion strategy is adopted. This paper presents a novel principled information fusion approach for dealing with multi-view multi-agent case, on the basis of Generalized Covariance Intersection (GCI). The proposed method can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic multi-object tracking scenarios demonstrate effectiveness of the proposed solution.