TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification

Jiyong Oh, Kil-Taek Lim, Yun-Su Chung

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 Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 408-416. DOI: 10.5220/0010243304080416


in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2021},
pages={408-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010243304080416},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification
SN - 978-989-758-486-2
AU - Oh J.
AU - Lim K.
AU - Chung Y.
PY - 2021
SP - 408
EP - 416
DO - 10.5220/0010243304080416