The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction
Abstract
Human trajectory prediction is challenging and critical in various applications (e.g., autonomous vehicles and social robots). Because of the continuity and foresight of the pedestrian movements, the moving pedestrians in crowded spaces will consider both spatial and temporal interactions to avoid future collisions. However, most of the existing methods ignore the temporal correlations of interactions with other pedestrians involved in a scene. In this work, we propose a Spatial-Temporal Graph Attention network (STGAT), based on a sequence-to-sequence architecture to predict future trajectories of pedestrians. Besides the spatial interactions captured by the graph attention mechanism at each time-step, we adopt an extra LSTM to encode the temporal correlations of interactions. Through comparisons with state-of-the-art methods, our model achieves superior performance on two publicly available crowd datasets (ETH and UCY) and produces more "socially" plausible trajectories for pedestrians.
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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Sequence to Sequence Learning with Neural Networks
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Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
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The graph neural network model
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Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 8,657
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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Graph neural networks: A review of methods and applications
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Social force model for pedestrian dynamics
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
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“Recently, some meth-ods [27] [40] in the field of action recognition have made significant progress by applying GNNs to spatial-temporal”
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Cited by
A Survey on Trajectory-Prediction Methods for Autonomous Driving
Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., & Chen, H. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3167103 · Cited by 677
Self-citationHuman motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Yuan, Y., Weng, X., Ou, Y., & Kitani, K. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00967 · Cited by 631
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
Kothari, P., Kreiss, S., & Alahi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3069362 · Cited by 267
ATPFL: Automatic Trajectory Prediction Model Design under Federated Learning Framework
Wang, C., Chen, X., Wang, J., & Wang, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00645 · Cited by 45
Metadata
- Authors
- Huang, Y., Bi, H., Li, Z., Mao, T., & Wang, Z.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 6271-6280
- OpenAlex
- W2991653934
- Cited by
- 658
- References
- 43
- FWCI
- 126.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Huang, Y., Bi, H., Li, Z., Mao, T., & Wang, Z. (2019). STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00637
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