In this article, 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 labeled Bernoulli component is created, and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another 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.
An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking
IEEE Transactions on Aerospace and Electronic Systems ; 58 , 6 ; 5256-5275
2022-12-01
1722160 byte
Article (Journal)
Electronic Resource
English
Deep Learning for Model-Based Multiobject Tracking
IEEE | 2023
|Multiobject tracking in video using a trisection paradigm
British Library Online Contents | 2007
|Interactive 3D shape design — Multipatch and Multiobject
Emerald Group Publishing | 1973
|