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Ruochen Li

Durham University
PhD (Co-supervised with )
, 2021 - Present

Durham University
, UK
  • Research interests: Crowd Analysis, Trajectory Prediction, Deep Learning

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Grants Involved

Publications with the Team

BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction
BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction  Impact Factor: 10.2† Top 10% Journal in Computer Science, Artificial Intelligence†   
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2025
Ruochen Li, Stamos Katsigiannis, Tae-Kyun Kim and Hubert P. H. Shum
Webpage Cite This Plain Text 
Ruochen Li, Stamos Katsigiannis, Tae-Kyun Kim and Hubert P. H. Shum, "BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction," IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2025.
Bibtex 
@article{li25bpsgcn,
 author={Li, Ruochen and Katsigiannis, Stamos and Kim, Tae-Kyun and Shum, Hubert P. H.},
 journal={IEEE Transactions on Neural Networks and Learning Systems},
 title={BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction},
 year={2025},
 numpages={15},
 doi={10.1109/TNNLS.2025.3545268},
 publisher={IEEE},
}
RIS 
TY  - JOUR
AU  - Li, Ruochen
AU  - Katsigiannis, Stamos
AU  - Kim, Tae-Kyun
AU  - Shum, Hubert P. H.
T2  - IEEE Transactions on Neural Networks and Learning Systems
TI  - BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction
PY  - 2025
DO  - 10.1109/TNNLS.2025.3545268
PB  - IEEE
ER  - 
 Paper Supplementary Material
Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction  Impact Factor: 8.3† Top 10% Journal in Engineering, Electrical & Electronic†   
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2025
Ruochen Li, Tanqiu Qiao, Stamos Katsigiannis, Zhanxing Zhu and Hubert P. H. Shum
Webpage Cite This Plain Text 
Ruochen Li, Tanqiu Qiao, Stamos Katsigiannis, Zhanxing Zhu and Hubert P. H. Shum, "Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction," IEEE Transactions on Circuits and Systems for Video Technology, IEEE, 2025.
Bibtex 
@article{li25unified,
 author={Li, Ruochen and Qiao, Tanqiu and Katsigiannis, Stamos and Zhu, Zhanxing and Shum, Hubert P. H.},
 journal={IEEE Transactions on Circuits and Systems for Video Technology},
 title={Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction},
 year={2025},
 doi={10.1109/TCSVT.2025.3539522},
 publisher={IEEE},
}
RIS 
TY  - JOUR
AU  - Li, Ruochen
AU  - Qiao, Tanqiu
AU  - Katsigiannis, Stamos
AU  - Zhu, Zhanxing
AU  - Shum, Hubert P. H.
T2  - IEEE Transactions on Circuits and Systems for Video Technology
TI  - Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
PY  - 2025
DO  - 10.1109/TCSVT.2025.3539522
PB  - IEEE
ER  - 
 Paper GitHub
Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos
Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos  Impact Factor: 7.5† Top 25% Journal in Computer Science, Artificial Intelligence†   
Expert Systems with Applications (ESWA), 2025
Tanqiu Qiao, Ruochen Li, Frederick W. B. Li, Yoshiki Kubotani, Shigeo Morishima and Hubert P. H. Shum
Webpage Cite This Plain Text 
Tanqiu Qiao, Ruochen Li, Frederick W. B. Li, Yoshiki Kubotani, Shigeo Morishima and Hubert P. H. Shum, "Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos," Expert Systems with Applications, pp. 128344, Elsevier, 2025.
Bibtex 
@article{qiao25geometric,
 author={Qiao, Tanqiu and Li, Ruochen and Li, Frederick W. B. and Kubotani, Yoshiki and Morishima, Shigeo and Shum, Hubert P. H.},
 journal={Expert Systems with Applications},
 title={Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos},
 year={2025},
 pages={128344},
 doi={10.1016/j.eswa.2025.128344},
 issn={0957-4174},
 publisher={Elsevier},
}
RIS 
TY  - JOUR
AU  - Qiao, Tanqiu
AU  - Li, Ruochen
AU  - Li, Frederick W. B.
AU  - Kubotani, Yoshiki
AU  - Morishima, Shigeo
AU  - Shum, Hubert P. H.
T2  - Expert Systems with Applications
TI  - Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos
PY  - 2025
SP  - 128344
EP  - 128344
DO  - 10.1016/j.eswa.2025.128344
SN  - 0957-4174
PB  - Elsevier
ER  - 
 Paper
From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos
From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos    H5-Index: 56#  
Proceedings of the 2024 International Conference on Pattern Recognition (ICPR), 2024
Tanqiu Qiao, Ruochen Li, Frederick W. B. Li and Hubert P. H. Shum
Webpage Cite This Plain Text 
Tanqiu Qiao, Ruochen Li, Frederick W. B. Li and Hubert P. H. Shum, "From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos," in ICPR '24: Proceedings of the 2024 International Conference on Pattern Recognition, Kolkata, India, 2024.
Bibtex 
@inproceedings{qiao24from,
 author={Qiao, Tanqiu and Li, Ruochen and Li, Frederick W. B. and Shum, Hubert P. H.},
 booktitle={Proceedings of the 2024 International Conference on Pattern Recognition},
 series={ICPR '24},
 title={From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos},
 year={2024},
 location={Kolkata, India},
}
RIS 
TY  - CONF
AU  - Qiao, Tanqiu
AU  - Li, Ruochen
AU  - Li, Frederick W. B.
AU  - Shum, Hubert P. H.
T2  - Proceedings of the 2024 International Conference on Pattern Recognition
TI  - From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos
PY  - 2024
ER  - 
 Paper Supplementary Material
Multiclass-SGCN: Sparse Graph-Based Trajectory Prediction with Agent Class Embedding
Multiclass-SGCN: Sparse Graph-Based Trajectory Prediction with Agent Class Embedding    H5-Index: 66#  Citation: 15#
Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), 2022
Ruochen Li, Stamos Katsigiannis and Hubert P. H. Shum
Webpage Cite This Plain Text 
Ruochen Li, Stamos Katsigiannis and Hubert P. H. Shum, "Multiclass-SGCN: Sparse Graph-Based Trajectory Prediction with Agent Class Embedding," in ICIP '22: Proceedings of the 2022 IEEE International Conference on Image Processing, pp. 2346-2350, Bordeaux, France, IEEE, Oct 2022.
Bibtex 
@inproceedings{li22multiclasssgcn,
 author={Li, Ruochen and Katsigiannis, Stamos and Shum, Hubert P. H.},
 booktitle={Proceedings of the 2022 IEEE International Conference on Image Processing},
 series={ICIP '22},
 title={Multiclass-SGCN: Sparse Graph-Based Trajectory Prediction with Agent Class Embedding},
 year={2022},
 month={10},
 pages={2346--2350},
 numpages={5},
 doi={10.1109/ICIP46576.2022.9897644},
 publisher={IEEE},
 location={Bordeaux, France},
}
RIS 
TY  - CONF
AU  - Li, Ruochen
AU  - Katsigiannis, Stamos
AU  - Shum, Hubert P. H.
T2  - Proceedings of the 2022 IEEE International Conference on Image Processing
TI  - Multiclass-SGCN: Sparse Graph-Based Trajectory Prediction with Agent Class Embedding
PY  - 2022
Y1  - 10 2022
SP  - 2346
EP  - 2350
DO  - 10.1109/ICIP46576.2022.9897644
PB  - IEEE
ER  - 
 Paper GitHub

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