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Most recent successes on forecasting the people motion are based on LSTM models and all most recent progress has been achieved by modelling the social interaction among people and the people interaction with the scene.
H. Akaike, “Fitting autoregressive models for prediction,” Annals of the institute of Statistical Mathematics , vol. 21, no. 1, pp. 243–247, 1969
1969
Earlier work this paper cites.
M. B. Priestley, Spectral analysis and time series . Academic press, 1981
1981
Earlier work this paper cites.
P. McCullagh and J. A. Nelder, “Generalized linear models, no. 37 in monograph on statistics and applied probability,” 1989
1989
Earlier work this paper cites.
D. Helbing and P. Molnar, “Social force model for,” Physical review E , vol. 51, no. 5, p. 4282, 1995
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
C. K. I. Williams, “Prediction with gaussian processes: From linear regression to linear prediction and beyond,” in Learning in graphical models . Springer, 1998, pp. 599–621
1998
Earlier work this paper cites.
H. Chen, S. Grant-Muller, L. Mussone, and F. Montgomery, “A study of hybrid neural network approaches and the effects of missing data on traffic forecasting,” Neural Computing & Applications , vol. 10, no. 3, pp. 277–286, 2001
2001
Earlier work this paper cites.
J. Quiñonero-Candela and C. E. Rasmussen, “A unifying view of sparse approximate gaussian process regression,” Journal of Machine Learning Research , vol. 6, no. 12, pp. 1939–1959, 2005
2005
Earlier work this paper cites.
A. Lerner, Y. Chrysanthou, and D. Lischinski, “Crowds by example,” in Computer Graphics Forum , 2007
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
B. T. Morris and M. M. Trivedi, “A survey of vision-based trajectory learning and analysis for surveillance,” IEEE Trans. on Circuits and Systems for Video Technology , vol. 18, no. 8, pp. 1114–1127, 2008
2008
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 ICCV , 2009
2009
Earlier work this paper cites.
J. Ferryman and A. Shahrokni, “Pets2009: Dataset and challenge,” in 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance , Dec 2009, pp. 1–6
2009
Earlier work this paper cites.
P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in IROS , 2010
2010
Earlier work this paper cites.
K. Kitani, B. Ziebart, J. Bagnell, and M. Hebert, “Activity forecasting,” in ECCV , 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
P. C. Rodrigues and M. De Carvalho, “Spectral modeling of time series with missing data,” Applied Mathematical Modelling , vol. 37, no. 7, pp. 4676–4684, 2013
2013
Earlier work this paper cites.
O. Anava, E. Hazan, and A. Zeevi, “Online time series prediction with missing data,” in International Conference on Machine Learning , 2015, pp. 2191–2199
2015
Cited alongside, same era.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human trajectory prediction in crowded spaces,” in CVPR , 2016
2016
Cited alongside, same era.
H. Su, Y. Dong, J. Zhu, H. Ling, and B. Zhang, “Crowd scene understanding with coherent recurrent neural networks,” in IJCAI , 2016
2016
Cited alongside, same era.
A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, “Learning social etiquette: Human trajectory understanding in crowded scenes,” in European conference on computer vision . Springer, 2016, pp. 549–565
2016
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Transformer attention is all you need,” in NIPS , 2017
2018
Later among the works it 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,” in Advances in Neural Information Processing Systems , 2019, pp. 137–146
2019
Later among the works it cites.
B. Ivanovic and M. Pavone, “The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 2375–2384
2019
Later among the works it 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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 1349–1358
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
H. Su, J. Zhu, Y. Dong, and B. Zhang, “Forecast the plausible paths in crowd scenes,” in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 , 2017, pp. 2772–2778. [Online]. Available: https://doi.org/10.24963/ijcai.2017/386
2017
Cited alongside, same era.
S.-H. Bae and K.-J. Yoon, “Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 3, pp. 595–610, 2017
2017
Cited alongside, same era.
A. Sadeghian, V. Kosaraju, A. Gupta, S. Savarese, and A. Alahi, “Trajnet: Towards a benchmark for human trajectory prediction,” arXiv preprint , 2018
2018
Cited alongside, same era.
A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, cONF
2018
Cited alongside, same era.
2018
Cited alongside, same era.
W. Luo, B. Yang, and R. Urtasun, “Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3569–3577
2018
Cited alongside, same era.
2019
Later among the works it cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert pre-training of deep bidirectional transformers for language understanding,” 2019
2019
Later among the works it cites.
R. Child, S. Gray, A. Radford, and I. Sutskever, “Generating long sequences with sparse transformers,” arXiv preprint:1904.10509 , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
P. Zhang, W. Ouyang, P. Zhang, J. Xue, and N. Zheng, “Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 085–12 094
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
“LSTM MATLAB implementation,” https://it.mathworks.com/help/deeplearning/ug/long-short-term-memory-networks.html , accessed: 2019-11-08
2019
Later among the works it cites.
2020
Closest in time.
C. Schöller, V. Aravantinos, F. Lay, and A. Knoll, “What the constant velocity model can teach us about pedestrian motion prediction,” IEEE Robotics and Automation Letters , 2020
2020
Closest in time.
2020
Closest in time.
S. Haddad and S.-K. Lam, “Self-growing spatial graph networks for pedestrian trajectory prediction,” in The IEEE Winter Conference on Applications of Computer Vision , 2020, pp. 1151–1159
2020
Closest in time.
D. Ridel, N. Deo, D. Wolf, and M. Trivedi, “Scene compliant trajectory forecast with agent-centric spatio-temporal grids,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2816–2823, 2020
2020
Closest in time.