Variational Tracking and Redetection for Closely-Spaced Objects in Heavy Clutter

• Published in: IEEE transactions on aerospace and electronic systems Vol. 60; no. 4; pp. 5286 - 5311
• Main Authors: Gan, Runze, Li, Qing, Godsill, Simon J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Atmospheric measurements; Bayes methods; Clutter; Coordinate ascent variational inference (CAVI); data association; Extreme values; Government; multiple object tracking; nonhomogeneous Poisson process (NHPP); Particle measurements; Sea measurements; Time measurement; track loss recovery; Tracking; variational bayes
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2024.3394464

The nonhomogeneous 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 track multiple objects efficiently and reliably 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 article presents a variational Bayes association-based NHPP tracker (VB-AbNHPP) that can efficiently track a fixed and known number of objects, perform data association, and learn object and clutter rates with a parallelizable implementation. Moreover, a variational localization strategy is proposed, which enables rapid rediscovery of missed objects 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.