IMGCN: interpretable masked graph convolution network for pedestrian trajectory prediction-Reference-Cited by-同舟云学术

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IMGCN: interpretable masked graph convolution network for pedestrian trajectory prediction

Published:2024-08-13 Issue:1 Volume:12 Page:
ISSN:2168-0566
Container-title:Transportmetrica B: Transport Dynamics
language:en
Short-container-title:Transportmetrica B: Transport Dynamics

Author:

Chen Wangxing¹,Sang Haifeng¹^ORCID,Wang Jinyu¹,Zhao Zishan¹

Affiliation:

1. School of Information Science and Engineering, Shenyang University of Technology, Shenyang, People's Republic of China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province

Scientific Research Project of Liaoning Provincial Department of Education

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/21680566.2024.2389896

Reference57 articles.

1. Alahi Alexandre Kratarth Goel Vignesh Ramanathan Alexandre Robicquet Li Fei-Fei and Silvio Savarese 2016. “Social LSTM: Human Trajectory Prediction in Crowded Spaces.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 961–971. Las Vegas: IEEE. https://doi.org/10.1109/cvpr.2016.110

2. Amirian Javad Jean-Bernard Hayet and Julien Pettré. 2019. “Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with Gans.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Long Beach: IEEE. https://doi.org/10.1109/cvprw.2019.00359

3. Bütepage Judith Hedvig Kjellström and Danica Kragic. 2018. “Anticipating Many Futures: Online Human Motion Prediction and Generation for Human-Robot Interaction.” In 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane: IEEE. https://doi.org/10.1109/icra.2018.8460651.

4. Chen Guangyi Junlong Li Jiwen Lu and Jie Zhou. 2021. “Human Trajectory Prediction via Counterfactual Analysis.” In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Montreal: IEEE. https://doi.org/10.1109/iccv48922.2021.00968.

5. STIGCN: spatial–temporal interaction-aware graph convolution network for pedestrian trajectory prediction

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