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Authors: Jiyong Oh ; Kil-Taek Lim and Yun-Su Chung

Affiliation: Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), Daegu, Korea

Keyword(s): Vehicle Trajectory Classification, TrajNet, Deep Neural Network, Intelligent Transportation System.

Abstract: Vehicle trajectory classification plays an important role in intelligent transportation systems because it can be utilized in traffic flow estimation at an intersection and anomaly detection such as traffic accidents and violations of traffic regulations. In this paper, we propose a new neural network architecture for vehicle trajectory classification by modifying the PointNet architecture, which was proposed for point cloud classification and semantic segmentation. The modifications are derived based on analyzing the differences between the properties of vehicle trajectory and point cloud. We call the modified network TrajNet. It is demonstrated from experiments using three public datasets that TrajNet can classify vehicle trajectories faster and more slightly accurate than the conventional networks used in the previous studies.

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Paper citation in several formats:
Oh, J.; Lim, K. and Chung, Y. (2021). TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 408-416. DOI: 10.5220/0010243304080416

@conference{icpram21,
author={Jiyong Oh. and Kil{-}Taek Lim. and Yun{-}Su Chung.},
title={TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={408-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010243304080416},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification
SN - 978-989-758-486-2
IS - 2184-4313
AU - Oh, J.
AU - Lim, K.
AU - Chung, Y.
PY - 2021
SP - 408
EP - 416
DO - 10.5220/0010243304080416
PB - SciTePress

- Science and Technology Publications, Lda.
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