Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter: Supplementary Materials

TL;DR

This paper introduces a variational Bayes tracker based on NHPP that efficiently tracks and redetects closely-spaced objects in heavy clutter, improving accuracy and robustness in challenging scenarios.

Contribution

It presents a novel variational Bayes data association method with a localisation strategy for rapid redetection in cluttered environments.

Findings

Outperforms existing trackers in accuracy and efficiency.

Enables rapid rediscovery of missed targets.

Automatically detects and recovers from track loss.

Abstract

The non-homogeneous Poisson process (NHPP) is a widely used measurement model that allows for an object to generate multiple measurements over time. However, it can be difficult to efficiently and reliably track multiple objects under this NHPP model in scenarios with a high density of closely-spaced objects and heavy clutter. Therefore, based on the general coordinate ascent variational filtering framework, this paper presents a variational Bayes association-based NHPP tracker (VB-AbNHPP) that can efficiently perform tracking, data association, and learning of target and clutter rates with a parallelisable implementation. In addition, a variational localisation strategy is proposed, which enables rapid rediscovery of missed targets from a large surveillance area under extremely heavy clutter. This strategy is integrated into the VB-AbNHPP tracker, resulting in a robust methodology that can automatically detect and recover from track loss. This tracker demonstrates improved tracking performance compared with existing trackers in challenging scenarios, in terms of both accuracy and efficiency.