Hypothesis Exploration in Multiple Hypothesis Tracking with Multiple Clusters

Finding the most probable posterior hypotheses is a core task in hypothesis-oriented multiple hypothesis tracking (HO-MHT), and also in related tracking methods such as the Poisson multi-Bernoulli mixture (PMBM) filter. The traditional approach is to find the M best new hypotheses for each parent hy...

Full description

Saved in:
Bibliographic Details
Published in2022 25th International Conference on Information Fusion (FUSION) pp. 1 - 8
Main Authors Brekke, Edmund Forland, Tokle, Lars-Christian Ness
Format Conference Proceeding
LanguageEnglish
Published International Society of Information Fusion 04.07.2022
Subjects
cluster management
Clustering algorithms
data association
Data models
Distance measurement
Filtering algorithms
Information filters
M-best assignment
multiple hypothesis tracking
Poisson multi-Bernoulli mixture filter
Systematics
Task analysis
Online AccessGet full text

Cover

More Information
Summary:Finding the most probable posterior hypotheses is a core task in hypothesis-oriented multiple hypothesis tracking (HO-MHT), and also in related tracking methods such as the Poisson multi-Bernoulli mixture (PMBM) filter. The traditional approach is to find the M best new hypotheses for each parent hypothesis by means of Murty's algorithm. In this paper we instead present an algorithm for finding the M best hypotheses ranging over all parent hypotheses. The algorithm is developed in the more general context of cluster management, where the goal is to merge several parent clusters, and to find the M best posterior hypotheses in any such supercluster.
DOI:10.23919/FUSION49751.2022.9841311