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

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