A Graph Neural Network Approach for Solving the Ranked Assignment Problem in Multi-Object Tracking
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Date
2024-07-15
Authors
Dehler Robin
Herrmann Martin
Strohbeck Jan
Buchholz Michael
Journal Title
Journal ISSN
Volume Title
Publication Type
Beitrag zu einer Konferenz
Published in
2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Abstract
Associating measurements with tracks is a crucial step in Multi-Object Tracking (MOT) to guarantee the safety of autonomous vehicles. To manage the exponentially growing number of track hypotheses, truncation becomes necessary. In the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter application, this truncation typically involves the ranked assignment problem, solved by Murty’s algorithm or the Gibbs sampling approach, both with limitations in terms of complexity or accuracy, respectively. With the motivation to improve these limitations, this paper addresses the ranked assignment problem arising from data association tasks with an approach that employs Graph Neural Networks (GNNs). The proposed Ranked Assignment Prediction Graph Neural Network (RAPNet) uses bipartite graphs to model the problem, harnessing the computational capabilities of deep learning. The conclusive evaluation compares the RAPNet with Murty’s algorithm and the Gibbs sampler, showing accuracy improvements compared to the Gibbs sampler.
Description
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Institutions
Institut für Mess-, Regel- und Mikrotechnik
Citation
DFG Project uulm
EU Project THU
EVENTS / ReliablE in-Vehicle pErception and decisioN-making in complex environmenTal conditionS / EC / HE / 101069614
PoDIUM / PDI connectivity and cooperation enablers building trust and sustainability for CCAM / EC / HE / 101069547
PoDIUM / PDI connectivity and cooperation enablers building trust and sustainability for CCAM / EC / HE / 101069547
Other projects THU
AUTOtech.agil / Verbundprojekt MANNHEIM-AUTOtech.agil: Architektur und Technologien zur Orchestrierung automobiltechnischer Agilität / BMBF / 01IS22088W
License
CC BY 4.0 International
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DOI external
DOI external
10.1109/IV55156.2024.10588674
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DFG Project THU
item.page.thu.projectEU
item.page.thu.projectOther
Series
Keywords
Data Association, Graph Neural Network, Zuordnungsproblem, Deep Learning, Tracking (Engineering), Assignment problems (Programming), Deep learning (Machine learning), Neural networks (Computer science), DDC 620 / Engineering & allied operations