Comparison of Credal Assignment Algorithms in Kinematic Data Tracking Context
| Autor: | François Delmotte, Samir Hachour, David Mercier |
|---|---|
| Přispěvatelé: | Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A), Université d'Artois (UA) |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Information Processing and Management of Uncertainty in Knowledge-Based Systems Information Processing and Management of Uncertainty in Knowledge-Based Systems, 444, Springer International Publishing, pp.200-211, 2014, Communications in Computer and Information Science, ⟨10.1007/978-3-319-08852-5_21⟩ Information Processing and Management of Uncertainty in Knowledge-Based Systems ISBN: 9783319088518 IPMU (3) |
| DOI: | 10.1007/978-3-319-08852-5_21⟩ |
| Popis: | This paper compares several assignment algorithms in a multi-target tracking context, namely: the optimal Global Nearest Neighbor algorithm (GNN) and a few based on belief functions. The robustness of the algorithms are tested in different situations, such as: nearby targets tracking, targets appearances management. It is shown that the algorithms performances are sensitive to some design parameters. It is shown that, for kinematic data based assignment problem, the credal assignment algorithms do not outperform the standard GNN algorithm. |
| Databáze: | OpenAIRE |
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