The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
Open accessAbstract
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans. Pedestrian trajectories are not only influenced by the pedestrian itself but also by interaction with surrounding objects. Previous methods modeled these interactions by using a variety of aggregation methods that integrate different learned pedestrians states. We propose the Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN), which substitutes the need of aggregation methods by modeling the interactions as a graph. Our results show an improvement over the state of art by 20% on the Final Displacement Error (FDE) and an improvement on the Average Displacement Error (ADE) with 8.5 times less parameters and up to 48 times faster inference speed than previously reported methods. In addition, our model is data efficient, and exceeds previous state of the art on the ADE metric with only 20% of the training data. We propose a kernel function to embed the social interactions between pedestrians within the adjacency matrix. Through qualitative analysis, we show that our model inherited social behaviors that can be expected between pedestrians trajectories. Code is available at https://github.com/abduallahmohamed/Social-STGCNN.
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Cites
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410 · cited 3×
“One of the development related to our work is the ST-GCNN [27].”
BackgroundAn Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Bai, S., Kolter, J. Z., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1803.01271 · Cited by 4,655
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.110 · Cited by 3,483
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
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You'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 3×
“The model is trained on two human trajectory prediction datasets: ETH [21] and UCY [11].”
MethodDeepGCNs: Can GCNs Go As Deep As CNNs?
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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
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 341
Query-Centric Trajectory Prediction
Zhou, Z., Wang, J., Li, Y., & Huang, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01713 · Cited by 333
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Ye, J., Zhao, J., Ye, K., & Xu, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3043250 · Cited by 281
Metadata
- Authors
- Mohamed, A., Qian, K., Elhoseiny, M., & Claudel, C.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 14412-14420
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3006895970
- Cited by
- 989
- References
- 28
- FWCI
- 102.06
- Type
- conference paper
- Language
- en
- Open access
- green
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