參考文獻 |
[1] D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, vol. 407, no. 6803, pp. 487-490, 2000.
[2] J. van den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” Robotics Research, Berlin, Germany: Springer, pp. 3-19, 2011.
[3] Y. Yao, E. Atkins, M. J. Roberson, R. Vasudevan, and X. Du “BiTraP: bi-directional pedestrian trajectory prediction with Multi-modal goal estimation,” IEEE Robotics and Automation Letters, vol. 6, pp. 1463-1470, 2021.
[4] R. Akabane and Y. Kato, “Pedestrian trajectory prediction based on transfer learning for human-following mobile robots,” IEEE International Conference on Big Data, pp. 3453-3458, 2020.
[5] X. Song, K. Chen, X. Li, J. Sun, B. Hou, Y. Cui, B. Zhang, G. Xiong, and Z. Wang, “Pedestrian trajectory prediction based on deep convolutional lstm network,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3285-3302, 2021.
[6] G. Agrim, J. Justin, F. F. Li, S. Silvio, and A. Alexandre, “Social gan: socially acceptable trajectories with generative adversarial networks,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2255-2264, 2018.
[7] S. Kim, S. J. Guy, W. Liu, R. W. Lau, M. C. Lin, and D. Manocha, “Predicting pedestrian trajectories using velocity-space reasoning,” Algorithmic Foundations of Robotics X, pp. 609-623, 2013.
[8] Z. Chen, L. Wang, and N. H. C. Yung, “Adaptive human motion analysis and prediction,” Science Direct Pattern Recognition, vol. 44, no. 12, pp. 2902-2914, 2011.
[9] C. Barate, J. C. Nascimento, J. M. Lemos, and J. S. Marques, “Sparse motion fields for trajectory prediction,” Science Direct Pattern Recognition, vol. 110, 1107631, 2021.
[10] A. Alexandre, G. Kratarth, R. Vignesh, R. Alexandre, F. F. Li, and S. Silvio, “Social lstm: human trajectory prediction in crowded spaces,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 961-971, 2016.
[11] I. J. Goodfellow, J. P. Abadie, M. Mirza, B. Xu, D. W. Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in Neural Information Processing Systems, vol. 27, pp. 2672-2680, 2014.
[12] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” International Conference on Learning Representations, pp. 1-14, 2017.
[13] A. Vemula, K. Muelling, and J. Oh, “Social attention: modeling attention in human crowds,” IEEE International Conference on Robotics and Automation, pp. 4601-4607, 2018.
[14] F. Bartoli, G. Lisanti, L. Ballan, and A. D. Bimbo, “Context-aware trajectory prediction,” 24th International Conference on Pattern Recognition, pp. 1941-1946, 2018.
[15] Z. Huang, J. Wang, L. Pi, X. Song, L. Yang, “LSTM based trajectory prediction model for cyclist utilizing multiple interactions with environment,” Science Direct Pattern Recognition, vol. 112, 107800, 2021.
[16] M. Pfeiffer, G. Paolo, H. Sommer, J. Nieto, and R. Siegwart, “A data-driven model for interaction-aware pedestrian motion prediction in object cluttered environments,” IEEE International Conference on Robotics and Automation, pp. 1-8, 2018.
[17] K. Mangalam, Y. An, H. Girase, and J. Malik, “From goals, waypoints & paths to long term human trajectory forecasting,” IEEE/CVF International Conference on Computer Vision, pp. 15213-15222, 2021.
[18] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” International Conference on Medical image computing and computer-assisted intervention, pp. 234-241, 2015.
[19] K. Mangalam, H. Girase, S. Agarwal, K. H. Lee, E. Adeli, J. Malik, and A. Gaidon, “It is not the journey but the destination: end-point conditioned trajectory prediction,” European Conference on Computer Vision, pp. 759-776, 2020.
[20] Y. Lecun, P. Haffner, L. Bottou, and Y. Bengio, “Object recognition with gradient-based learning,” Shape, Contour and Grouping in Computer Vision, pp. 319–345, 1999.
[21] D. P. Kingma and J. L. Ba, “Adam: a method for stochastic optimization,” International Conference on Learning Representations, pp. 1-15, 2015.
[22] R. Liu, J. Lehman, P. Molino, F. P. Such, E. Frank, A. Sergeev, and J. Yosinski, “An intriguing failing of convolutional neural networks and the coorconv solution,” Neural Information Processing Systems, pp. 9628-9639, 2018.
[23] P. Baldi, “Autoencoders, unsupervised learning and deep architectures,” Unsupervised and Transfer Learning workshop, vol. 27, pp. 37-50, 2011.
[24] A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–17, 2020.
[25] A. C. Bovik, “Bilinear interpolation,” The Essential Guide to Image Processing, pp.43-68, 2009.
[26] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904-1916, 2015.
[27] D. A. Ckevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (elus), ” International Conference on Learning Representations, pp. 1-14, 2016.
[28] S. Pellegrini, A. Ess, K. Schindler, and K. Schindler, “You′ll never walk alone: modeling social behavior for multi-target tracking,” IEEE 12th International Conference on Computer Vision, pp. 261-268, 2009.
[29] A. Lerner, Y. Chrysanthou, and D. Lischinski, “Crowds by example,” Computer Graphics Forum, vol. 26, no. 3, pp. 655-664, 2007.
[30] A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, “Learning social etiquette: human trajectory understanding in crowded scenes,” European Conference on Computer Vision, pp.549-565, 2016.
[31] H. Caesar, J. Uijlings, and V. Ferrari, “Coco-stuff: thing and stuff classes in context,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1209-1218, 2018.
[32] R. Liang, Y. Li, X. Li, Y. Tang, J. Zhou, and W. Zou, “Temporal pyramid network for pedestrian trajectory prediction with multi-supervision,” 35th AAAI Conference on Artificial Intelligence, pp. 2029-2037, 2021.
[33] X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo, “Convolutional lstm network: a machine learning approach for precipitation nowcasting,” Advances in Neural Information Processing Systems, vol. 28, pp. 1-9, 2015.
[34] N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” IEEE International Conference on Image Processing, pp. 3645-3649, 2017.
[35] A. Bewley, G. Zongyuan, F. Ramos, and B. Upcroft, “Simple online and realtime tracking,” IEEE International Conference on Image Processing, pp. 3464-3468, 2016.
[36] B. Benfold and I. Reid, “Stable multi-target tracking in real-time surveillance video,” Conference on Computer Vision and Pattern Recognition, pp.3457-3464, 2011.
[37] J. Liang, L. Jiang, J. C. Niebles, A. G. Hauptmann, L. F. Fei, “Peeking into the future: predicting future person activities and locations in videos,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5718-5727, 2019.
[38] Y. Yuan, X. Weng, Y. Ou, and K. Kitani, “Agentformer: agent-aware transformers for socio-temporal multi-agent forecasting,” IEEE/CVF International Conference on Computer Vision, pp. 9793-9803, 2021.
[39] A. Vaswani, N. Shazzer, N. Parmar, J. Uskoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Neural Information Processing Systems, pp. 1-11, 2017. |