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Authors: Jaime B. Fernandez ; Suzanne Little and Noel E. O’connor

Affiliation: Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland

Keyword(s): Multiple Path Prediction, Traffic Scenes, LSTMs, MDNs, Time Series.

Abstract: This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predictin g a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye view). (More)

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Paper citation in several formats:
Fernandez, J. B., Little, S. and O’connor, N. E. (2020). Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models. In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-419-0; ISSN 2184-495X, SciTePress, pages 481-488. DOI: 10.5220/0009412204810488

@conference{vehits20,
author={Jaime B. Fernandez and Suzanne Little and Noel E. O’connor},
title={Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models},
booktitle={Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2020},
pages={481-488},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009412204810488},
isbn={978-989-758-419-0},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models
SN - 978-989-758-419-0
IS - 2184-495X
AU - Fernandez, J.
AU - Little, S.
AU - O’connor, N.
PY - 2020
SP - 481
EP - 488
DO - 10.5220/0009412204810488
PB - SciTePress

- Science and Technology Publications, Lda.
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