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Trajectory prediction is an important task that involves modeling the indeterminate nature of traffic actors to forecast future trajectories given the observed trajectory sequences.
C. Wei, L. Xie, X. Ren, Y. Xia, C. Su, J. Liu, Q. Tian, and A. L. Yuille, “Iterative reorganization with weak spatial constraints: Solving arbitrary jigsaw puzzles for unsupervised representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 1910–1919
1919
Earlier work this paper cites.
A. Lerner, Y. Chrysanthou, and D. Lischinski, “Crowds by example,” in Computer graphics forum , vol. 26, no. 3. Wiley Online Library, 2007, pp. 655–664
2007
Earlier work this paper cites.
S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool, “You’ll never walk alone: Modeling social behavior for multi-target tracking,” in 2009 IEEE 12th international conference on computer vision . IEEE, 2009, pp. 261–268
2009
Earlier work this paper cites.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 961–971
2016
Earlier work this paper cites.
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan, “Learning features by watching objects move,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2701–2710
2017
Earlier work this paper cites.
A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2255–2264
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel, “Neural relational inference for interacting systems,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, 10–15 Jul 2018, pp. 2688–2697. [Online]. Available: https://proceedings.mlr.press/v80/kipf18a.html
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 132–149
2018
Earlier work this paper cites.
A. Vemula, K. Muelling, and J. Oh, “Social attention: Modeling attention in human crowds,” in 2018 IEEE international Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 4601–4607
2018
Earlier work this paper cites.
A. Sadeghian, V. Kosaraju, A. Sadeghian, N. Hirose, H. Rezatofighi, and S. Savarese, “Sophie: An attentive gan for predicting paths compliant to social and physical constraints,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , no. CONF, 2019
2019
Earlier work this paper cites.
Y. Huang, H. Bi, Z. Li, T. Mao, and Z. Wang, “Stgat: Modeling spatial-temporal interactions for human trajectory prediction,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 6272–6281
2019
Earlier work this paper cites.
V. Kosaraju, A. Sadeghian, R. Martín-Martín, I. Reid, H. Rezatofighi, and S. Savarese, “Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
B. Ivanovic and M. Pavone, “The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2375–2384
2019
Earlier work this paper cites.
Y. Hu, S. Chen, Y. Zhang, and X. Gu, “Collaborative motion prediction via neural motion message passing,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 6319–6328
2020
Earlier work this paper cites.
K. Mangalam, H. Girase, S. Agarwal, K.-H. Lee, E. Adeli, J. Malik, and A. Gaidon, “It is not the journey but the destination: Endpoint conditioned trajectory prediction,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 759–776
2020
Earlier work this paper cites.
C. Yu, X. Ma, J. Ren, H. Zhao, and S. Yi, “Spatio-temporal graph transformer networks for pedestrian trajectory prediction,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16 . Springer, 2020, pp. 507–523
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Mohamed, K. Qian, M. Elhoseiny, and C. Claudel, “Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 14 424–14 432
2020
Earlier work this paper cites.
T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16 . Springer, 2020, pp. 683–700
2020
Earlier work this paper cites.
F. Giuliari, I. Hasan, M. Cristani, and F. Galasso, “Transformer networks for trajectory forecasting,” in 2020 25th international conference on pattern recognition (ICPR) . IEEE, 2021, pp. 10 335–10 342
2021
Earlier work this paper cites.
J. Sekhon and C. Fleming, “Scan: A spatial context attentive network for joint multi-agent intent prediction,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 7, 2021, pp. 6119–6127
2021
Cited alongside, same era.
P. Kothari, S. Kreiss, and A. Alahi, “Human trajectory forecasting in crowds: A deep learning perspective,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–15, 2021
2021
Cited alongside, same era.
L. Shi, L. Wang, C. Long, S. Zhou, M. Zhou, Z. Niu, and G. Hua, “Sgcn: Sparse graph convolution network for pedestrian trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 8994–9003
2021
Cited alongside, same era.
Y. Yuan, X. Weng, Y. Ou, and K. M. Kitani, “Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9813–9823
2021
Cited alongside, same era.
P. Lv, W. Wang, Y. Wang, Y. Zhang, M. Xu, and C. Xu, “Ssagcn: social soft attention graph convolution network for pedestrian trajectory prediction,” IEEE transactions on neural networks and learning systems , 2023
2023
Closest in time.
P. Bhattacharyya, C. Huang, and K. Czarnecki, “Ssl-lanes: Self-supervised learning for motion forecasting in autonomous driving,” in Conference on Robot Learning . PMLR, 2023, pp. 1793–1805
2023
Closest in time.
L. Shi, L. Wang, C. Long, S. Zhou, W. Tang, N. Zheng, and G. Hua, “Representing multimodal behaviors with mean location for pedestrian trajectory prediction,” IEEE transactions on pattern analysis and machine intelligence , 2023
2023
Closest in time.
J. Sun, Y. Li, L. Chai, and C. Lu, “Stimulus verification is a universal and effective sampler in multi-modal human trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 22 014–22 023
2023
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H. Cheng, W. Liao, M. Y. Yang, B. Rosenhahn, and M. Sester, “Amenet: Attentive maps encoder network for trajectory prediction,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 172, pp. 253–266, 2021
2021
Cited alongside, same era.
M. Mendieta and H. Tabkhi, “Carpe posterum: A convolutional approach for real-time pedestrian path prediction,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 3, 2021, pp. 2346–2354
2021
Cited alongside, same era.
B. Yang, G. Yan, P. Wang, C.-Y. Chan, X. Song, and Y. Chen, “A novel graph-based trajectory predictor with pseudo-oracle,” IEEE transactions on neural networks and learning systems , vol. 33, no. 12, pp. 7064–7078, 2021
2021
Cited alongside, same era.
N. Shafiee, T. Padir, and E. Elhamifar, “Introvert: Human trajectory prediction via conditional 3d attention,” in Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern recognition , 2021, pp. 16 815–16 825
2021
Cited alongside, same era.
B. Pang, T. Zhao, X. Xie, and Y. N. Wu, “Trajectory prediction with latent belief energy-based model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 814–11 824
2021
Cited alongside, same era.
C. Xu, M. Li, Z. Ni, Y. Zhang, and S. Chen, “Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 6498–6507
2022
Cited alongside, same era.
T. Gu, G. Chen, J. Li, C. Lin, Y. Rao, J. Zhou, and J. Lu, “Stochastic trajectory prediction via motion indeterminacy diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 113–17 122
2022
Cited alongside, same era.
M. Lee, S. S. Sohn, S. Moon, S. Yoon, M. Kapadia, and V. Pavlovic, “Muse-vae: multi-scale vae for environment-aware long term trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2221–2230
2022
Cited alongside, same era.
Z. Zhou, J. Wang, Y.-H. Li, and Y.-K. Huang, “Query-centric trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 863–17 873
2023
Closest in time.
X. Zhong, X. Yan, Z. Yang, W. Huang, K. Jiang, R. W. Liu, and Z. Wang, “Visual exposes you: Pedestrian trajectory prediction meets visual intention,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Closest in time.
X. Chen, H. Zhang, Y. Hu, J. Liang, and H. Wang, “Vnagt: Variational non-autoregressive graph transformer network for multi-agent trajectory prediction,” IEEE Transactions on Vehicular Technology , 2023
2023
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C. Yang and Z. Pei, “Long-short term spatio-temporal aggregation for trajectory prediction,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 4, pp. 4114–4126, 2023
2023
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Y. Li, C. Xie, R. Liang, J. Du, J. Zhou, and X. Li, “A synchronous bi-directional framework with temporally dependent interaction modeling for pedestrian trajectory prediction,” IEEE Transactions on Network Science and Engineering , 2023
2023
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C. Zhang, Z. Ni, and C. Berger, “Spatial-temporal-spectral lstm: A transferable model for pedestrian trajectory prediction,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Closest in time.
C. Xu, Y. Wei, B. Tang, S. Yin, Y. Zhang, S. Chen, and Y. Wang, “Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning,” Neural Networks , 2023
2023
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R. Wang, Z. Hu, X. Song, and W. Li, “Trajectory distribution aware graph convolutional network for trajectory prediction considering spatio-temporal interactions and scene information,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
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Y. Wang, P. Zhang, L. Bai, and J. Xue, “Fend: A future enhanced distribution-aware contrastive learning framework for long-tail trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1400–1409
2023
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X. Chen, F. Luo, F. Zhao, and Q. Ye, “Goal-guided and interaction-aware state refinement graph attention network for multi-agent trajectory prediction,” IEEE Robotics and Automation Letters , vol. 9, no. 1, pp. 57–64, 2023
2023
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J. Sun, Y. Li, L. Chai, and C. Lu, “Modality exploration, retrieval and adaptation for trajectory prediction,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
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C. Wong, B. Xia, Q. Peng, W. Yuan, and X. You, “Msn: multi-style network for trajectory prediction,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
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X. Shi, H. Zhang, W. Yuan, and R. Shibasaki, “Metatraj: meta-learning for cross-scene cross-object trajectory prediction,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
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D. I. S.-T. L. R. to Explain Human Actions, “Discovering intrinsic spatial-temporal logic rules to explain human actions,” Advances in Neural Information Processing Systems , 2023
2023
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M. Ye, J. Xu, X. Xu, T. Wang, T. Cao, and Q. Chen, “Bootstrap motion forecasting with self-consistent constraints,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8504–8514
2023
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2023
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W. Zhu, Y. Liu, M. Zhang, and Y. Yi, “Reciprocal consistency prediction network for multi-step human trajectory prediction,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
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C. Yang, H. Pan, W. Sun, and H. Gao, “Social self-attention generative adversarial networks for human trajectory prediction,” IEEE Transactions on Artificial Intelligence , 2023
2023
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