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

IEEE Access

Stefan Becker¹  Wolfgang Hubner¹  Michael Arens¹  Ronny Hug¹ 

[1] Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Fraunhofer IOSB), Ettlingen, Germany;

关键词: Benchmark testing;    data analysis;    data preprocessing;    machine learning algorithms;    pattern clustering;    prediction algorithms;   

DOI  :  10.1109/ACCESS.2021.3082904

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown