A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks

• URL: http://arxiv.org/abs/2004.04677v2
• Date: Thu, 28 May 2020 15:04:54 GMT
• Title: A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks
• Authors: Ronny Hug, Stefan Becker, Wolfgang H\"ubner and Michael Arens
• Abstract summary: An approach for determining a dataset representation in terms of a small set of distinguishable sub-sequences is proposed. A first proof of concept on synthetically generated and real-world datasets shows the viability of the approach.
• Score: 8.870188183999852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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