An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking

Abstract

We propose an efficient random finite set (RFS) based algorithm for multiobject tracking in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new LMB component is created and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, an LMB component is transferred back to the Poisson RFS if the corresponding existence probability falls below a threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.