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Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

: Hug, Ronny; Becker, Stefan; Hübner, Wolfgang; Arens, Michael

Volltext urn:nbn:de:0011-n-6361227 (2.2 MByte PDF)
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Erstellt am: 22.6.2021

IEEE access 9 (2021), S.77693-77704
ISSN: 2169-3536
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IOSB ()
benchmark testing; data analysis; data preprocessing; machine learning algorithms; pattern clustering; prediction algorithms

Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.