The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks. In order to enable a more informative assembly of a data basis, an approach for determining a dataset representation in terms of a small set of distinguishable prototypical sub-sequences is proposed. The approach employs a sequence alignment followed by a learning vector quantization (LVQ) stage. A first proof of concept on synthetically generated and real-world datasets shows the viability of the approach.
@article{arxiv.2004.04677,
title = {A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks},
author = {Ronny Hug and Stefan Becker and Wolfgang Hübner and Michael Arens},
journal= {arXiv preprint arXiv:2004.04677},
year = {2020}
}