Search Results for author: Thomas Kropfreiter

Found 6 papers, 0 papers with code

Multiobject Tracking for Thresholded Cell Measurements

no code implementations11 Mar 2024 Thomas Kropfreiter, Jason L. Williams, Florian Meyer

Our numerical results demonstrate the advantages of the proposed method that relies on thresholded cell measurements compared to the conventional multiobject tracking based on point measurements with and without AM.

To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods

no code implementations11 Aug 2023 Thomas Kropfreiter, Florian Meyer, David F. Crouse, Stefano Coraluppi, Franz Hlawatsch, Peter Willett

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT).

A BP Method for Track-Before-Detect

no code implementations3 Jul 2023 Mingchao Liang, Thomas Kropfreiter, Florian Meyer

Our proposed method is based on a new statistical model that introduces a new object hypothesis for each data cell of the raw sensor measurements.

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

no code implementations11 Sep 2021 Thomas Kropfreiter, Florian Meyer, Franz Hlawatsch

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.

Object

Track Coalescence and Repulsion: MHT, JPDA, and BP

no code implementations3 Sep 2021 Thomas Kropfreiter, Florian Meyer, Stefano Coraluppi, Craig Carthel, Rico Mendrzik, Peter Willett

In particular, JPDA suffers from the track coalescence effect, i. e., estimated tracks of targets in close proximity tend to merge and can become indistinguishable.

A Scalable Track-Before-Detect Method With Poisson/Multi-Bernoulli Model

no code implementations3 Sep 2021 Thomas Kropfreiter, Jason L. Williams, Florian Meyer

We propose a scalable track-before-detect (TBD) tracking method based on a Poisson/multi-Bernoulli model.

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