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Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding

Li, Ruochen; Katsigiannis, Stamos; Shum, Hubert P.H.

Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding Thumbnail


Authors

Ruochen Li ruochen.li@durham.ac.uk
PGR Student Doctor of Philosophy



Abstract

Trajectory prediction of road users in real-world scenarios is challenging because their movement patterns are stochastic and complex. Previous pedestrian-oriented works have been successful in modelling the complex interactions among pedestrians, but fail in predicting trajectories when other types of road users are involved (e.g., cars, cyclists, etc.), because they ignore user types. Although a few recent works construct densely connected graphs with user label information, they suffer from superfluous spatial interactions and temporal dependencies. To address these issues, we propose Multiclass-SGCN, a sparse graph convolution network based approach for multi-class trajectory prediction that takes into consideration velocity and agent label information and uses a novel interaction mask to adaptively decide the spatial and temporal connections of agents based on their interaction scores. The proposed approach significantly outperformed state-of-the-art approaches on the Stanford Drone Dataset, providing more realistic and plausible trajectory predictions.

Citation

Li, R., Katsigiannis, S., & Shum, H. P. (2022). Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding. In 2022 IEEE International Conference on Image Processing (ICIP) Proceedings (2346-2350). https://doi.org/10.1109/icip46576.2022.9897644

Conference Name ICIP 2022: IEEE International Conference in Image Processing
Conference Location Bordeaux, France
Start Date Oct 16, 2022
End Date Oct 19, 2022
Acceptance Date Jun 20, 2022
Online Publication Date Oct 18, 2022
Publication Date Oct 19, 2022
Deposit Date Jun 20, 2022
Publicly Available Date Mar 28, 2024
Pages 2346-2350
Series ISSN 1522-4880,2381-8549
Book Title 2022 IEEE International Conference on Image Processing (ICIP) Proceedings
ISBN 9781665496216
DOI https://doi.org/10.1109/icip46576.2022.9897644

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