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

• URL: http://arxiv.org/abs/2005.13934v4
• Date: Thu, 20 May 2021 08:17:40 GMT
• Title: Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
• Authors: Ronny Hug, Stefan Becker, Wolfgang H\"ubner, Michael Arens
• Abstract summary: We propose an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets.
• Score: 8.870188183999852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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