Abstract
Predicting pedestrian’s future behavior in a crowd plays an important role in many fields. Such as autonomous driving, machine navigation, video surveillance, and intelligent security systems. This is very challenging because pedestrian motion can be easily influenced by surrounding pedestrians’ interactions. In previous works, researchers use these interactions to make prediction more effective. However, the previous work set fixed-length input in their models. In this way, they ignore shorter pedestrian trajectories. This approach leads to insufficient feature information and inaccurately prediction in some scenarios. In this paper, we propose an Autoencoder-based model for pedestrian trajectory prediction of variable length (ASTRAL). At first, we use the autoencoder to process pedestrian data with variable-length trajectories. And then, we use the optimized multi-head attention mechanism to extract the interactions between neighbors. Finally, we use LSTM to decode vectors and make predictions. In particular, we fine-tune the model to make its performance better. We test our model and the state-of-the-art methods on the public benchmark datasets. Compared with others, our model improves ADE (average displacement error) and FDE (final displacement error) by \(9\%\) and \(33\%\) respectively. Therefore, our model is better than previous works, and we can predict the future trajectory of pedestrians more effectively.
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References
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961–971 (2016)
Antonini, G., Bierlaire, M., Weber, M.: Discrete Choice Models of Pedestrian Walking Behavior. Transp. Res. Part B Methodol. 40, 667–687 (2006)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Chen, X., et al.: SCSG attention: a self-centered star graph with attention for pedestrian trajectory prediction. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12681, pp. 422–438. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73194-6_29
Ferrer, G., Garrell, A., Sanfeliu, A.: Robot companion: a social-force based approach with human awareness-navigation in crowded environments. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1688–1694 (2013). https://doi.org/10.1109/IROS.2013.6696576
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2255–2264 (2018)
Haddad, S., Wu, M., Wei, H., Lam, S.K.: Situation-aware pedestrian trajectory prediction with spatio-temporal attention model. arXiv preprint arXiv:1902.05437 (2019)
Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transpo. Sci. 39(1), 1–24 (2005)
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997)
Leal-Taixé, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3542–3549 (2014)
Miao, Y., Gowayyed, M., Metze, F.: EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 167–174 (2015). https://doi.org/10.1109/ASRU.2015.7404790
Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 452–465. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_33
Raksincharoensak, P., Hasegawa, T., Nagai, M.: Motion planning and control of autonomous driving intelligence system based on risk potential optimization framework. Int. J. Autom. Eng. 7(AVEC14), 53–60 (2016)
Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., Savarese, S.: 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, pp. 1349–1358 (2019)
Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1577–1586. Association for Computational Linguistics, Beijing, China, July 2015. https://doi.org/10.3115/v1/P15-1152, https://aclanthology.org/P15-1152
Stahlberg, F.: Neural machine translation: a review. J. Artif. Intell. Res. 69, 343–418 (2020)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp. 3104–3112 (2014)
Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. ACM Trans. Graph. 25(3), 1160–1168 (2006)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., Stolcke, A.: The microsoft 2017 conversational speech recognition system. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5934–5938 (2018). https://doi.org/10.1109/ICASSP.2018.8461870
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Diao, Y., Su, Y., Zeng, X., Chen, X., Liu, S., Su, H. (2022). Astral: An Autoencoder-Based Model for Pedestrian Trajectory Prediction of Variable-Length. In: Rage, U.K., Goyal, V., Reddy, P.K. (eds) Database Systems for Advanced Applications. DASFAA 2022 International Workshops. DASFAA 2022. Lecture Notes in Computer Science, vol 13248. Springer, Cham. https://doi.org/10.1007/978-3-031-11217-1_16
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