Markov Chain Monte Carlo Multi-Scan Data Association for Sets of Trajectories
Artikel i vetenskaplig tidskrift, 2024

This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories. Specifically, we present two offline implementations of the trajectory Poisson multi-Bernoulli mixture (TPMBM) filter for batch data based on Markov chain Monte Carlo (MCMC) sampling of the data association hypotheses. In contrast to online TPMBM implementations, the proposed offline implementations solve a large-scale, multi-scan data association problem across the entire time interval of interest, and therefore they can fully exploit all the measurement information available. Furthermore, by leveraging the efficient hypothesis structure of TPMBM filters, the proposed implementations compare favorably with other MCMC-based multi-object tracking algorithms. Simulation results show that the TPMBM implementation using the Metropolis-Hastings algorithm presents state-of-the-art multiple trajectory estimation performance.

Proposals

Current measurement

Markov chain Monte Carlo

sets of trajectories

smoothing

Multiple object tracking

Estimation

Monte Carlo methods

Standards

Time measurement

data association

Trajectory

Författare

Yuxuan Xia

Zenseact AB

Angel Garcia

University of Liverpool

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN) 15579603 (eISSN)

Vol. In Press

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Signalbehandling

DOI

10.1109/TAES.2024.3419785

Mer information

Senast uppdaterat

2024-07-16