Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter: Supplementary Materials
Runze Gan, Qing Li, Simon Godsill

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…
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Taxonomy
TopicsTarget Tracking and Data Fusion in Sensor Networks · Infrared Target Detection Methodologies · Video Surveillance and Tracking Methods
