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

: Hug, R.; Becker, S.; Hübner, W.; Arens, M.


Chen, L. ; International Association for Pattern Recognition -IAPR-:
Representations, Analysis and Recognition of Shape and Motion from Imaging Data. 7th International Workshop, RFMI 2017 : Savoie, France, December 17-20, 2017, Revised Selected Papers
Cham: Springer Nature, 2019 (Communications in computer and information science 842)
ISBN: 978-3-030-19815-2 (Print)
ISBN: 978-3-030-19816-9 (Online)
ISBN: 3-030-19815-4
International Workshop on Representation, analysis and recognition of shape and motion FroM Image data (RFMI) <7, 2017, Savoie>
Fraunhofer IOSB ()

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.