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  4. A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks
 
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2020
Presentation
Title

A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks

Title Supplement
Accepted at LHMP 2020 Workshop (ICRA 2020)
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.
Author(s)
Hug, Ronny  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Becker, Stefan  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Hübner, Wolfgang  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Arens, Michael  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Conference
International Conference on Robotics and Automation (ICRA) 2020  
Workshop on Long-term Human Motion Prediction (LHMP) 2020  
File(s)
Download (1.93 MB)
Rights
Use according to copyright law
DOI
10.24406/publica-fhg-408421
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
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