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On the reliability of LSTM-MDL models for pedestrian trajectory prediction

 
: Hug, Ronny; Becker, Stefan; Hübner, Wolfgang; Arens, Michael

:
Volltext urn:nbn:de:0011-n-4819010 (4.0 MByte PDF)
MD5 Fingerprint: ea33e0b613ed5e9087904fab8bbaa5fa
Erstellt am: 8.2.2018


International Association for Pattern Recognition -IAPR-:
VIIth International Workshop on Representation, analysis and recognition of shape and motion FroM Image data, RFMI 2017. Accepted papers. Online resource : December, 17th to 20, 2017, Centre Paul-Langevin Aussois - Savoie, France
Savoie, 2017
http://www.arts-pi.org.tn/rfmi2017/program.html
15 S.
International Workshop on Representation, analysis and recognition of shape and motion FroM Image data (RFMI) <7, 2017, Savoie>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
recurrent neural network; pedestrian trajectory prediction; generative modeling

Abstract
Recurrent neural networks, like the LSTM model, have been applied to various sequence learning tasks with great success. Following this, it seems natural to use LSTM models for predicting future locations in object tracking tasks. In this paper, we evaluate an adaption of a LSTM-MDL model and investigate its reliability in the context of pedestrian trajectory prediction. Thereby, we demonstrate the fallacy of solely relying on prediction metrics for evaluating the model and how the models capabilities can lead to suboptimal prediction results. Towards this end, two experiments are provided. Firstly, the models prediction abilities are evaluated on publicly available surveillance datasets. Secondly, the capabilities of capturing motion patterns are examined. Further, we investigate failure cases and give explanations for observed phenomena, granting insight into the models reliability in tracking applications. Lastly, we give some hints how demonstrated shortcomings may be circumvented.

: http://publica.fraunhofer.de/dokumente/N-481901.html