Abstract
Pedestrian trajectory prediction is a challenging problem in the crowded and chaotic scenarios. Currently, the prediction error is still high because the input of Long Short-Term Memory (LSTM) network is a 1D vector, which cannot represent the spatial information of pedestrians. To tackle this, we propose to use tensors to represent the complex environmental information. Meanwhile, LSTM internal full connection is converted into full convolution to predict the spatiotemporal pedestrian trajectory sequences. The results show that our method reduces the displacement offset error better than recent works including Social-LSTM, SS-LSTM, CNN, Social-GAN, Scene-LSTM, providing more realistic trajectory prediction for the chaotic crowd.
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References
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Li, F.-F., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: CVPR, pp. 961–971, June 2016
Song, X., Han, D., Sun, J.: A data-driven neural network approach to simulate pedestrian movement. Physica A-Stat. Mech. Appl. 509(11), 827–844 (2018)
Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H.S., Chandraker, M.: DESIRE: distant future prediction in dynamic scenes with interacting agents. In: CVPR (2017)
Su, H., Zhu, J., Dong, Y., Zhang, B.: Forecast the plausible paths in crowd scenes. In: IJCAI, pp. 2772–2778 (2017)
Vemula, A., Muelling, K., Oh, J.: Modeling cooperative navigation in dense human crowds. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1685–1692, May 2017
Song, X., Xie, H., Sun, J., Han, D.: Simulation of pedestrian rotation dynamics near crowded exits. IEEE Trans. Intell. Transp. Syst. 20(8), 3142–3155 (2019)
Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: ICCV, pp. 261–268 (2009)
Song, X., Ma, L., Ma, Y.: Selfishness- and selflessness-based models of pedestrian room evacuation. Physica A-stat. Mech. Appl. 447(4), 455–466 (2016)
Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Soft + hardwired attention: an LSTM framework for human trajectory prediction and abnormal event detection. arXiv preprint arXiv:1702.05552 (2017)
Nam, H., Han, B.: Learning Multi-domain convolutional neural networks for visual tracking. In: Computer Vision and Pattern Recognition, pp. 4293–4302 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (2015)
Nikhil, N., Tran Morris, B.: Convolutional neural network for trajectory prediction. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Manh, H., Alaghband, G.: Scene-lstm: a model for human trajectory prediction. arXiv preprint arXiv:1808.04018 (2018)
Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: IEEE 12th International Conference on Computer Vision (ICCV), pp. 261–268 (2009)
Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg, T.L.: Who are you with and where are you going? In: CVPR, pp. 1345–1352 (2011)
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 961–971 (2016)
Vemula, A., Muelling, K., Oh, J.: Social attention: modeling attention in human crowds. arXiv:1710.04689 [cs] (2017)
Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-RNN: deep learning on spatio-temporal graphs. In: CVPR 2016, pp. 5308–5317 (2016)
Xue, H., Huynh, D.Q., Reynolds, M.: SS-LSTM: a hierarchical LSTM model for pedestrian trajectory prediction. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1186–1194 (2018)
Bartoli, F., Lisanti, G., Ballan, L., Del Bimbo, A.: Context-Aware Trajectory Prediction, arXiv:1705.02503 [cs], May 2017
Varshneya, D., Srinivasaraghavan, G.: Human Trajectory Prediction using Spatially aware Deep Attention Models. arXiv:1705.09436 [cs], May 2017
Pellegrini, S., Ess, A., Schindler, K., et al.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261–268. IEEE (2009)
Simonyan, K., Zisserman, A.: Very deep convolutional networks forlarge-scale image recognition. In: Proceedings of the International Conference on Learning Representations (2015)
Lerner, A., Chrysanthou, Y., Lischinski, D.L: Crowds by example. In: Computer Graphics Forum, vol. 26, no. (3), pp. 655–664. Blackwell Publishing Ltd, Oxford (2007)
Gupta, A., Johnson, J., Fei-Fei, L., et al.: 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)
Song, X., Shi, W., Ma, Y.: Impact of informal networks on opinion dynamics in hierarchically formal organization. Physica A-stat. Mech. Appl. 436(10), 916–924 (2015)
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Chen, K., Song, X., Yu, H. (2019). Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios. In: Tan, G., Lehmann, A., Teo, Y., Cai, W. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2019. Communications in Computer and Information Science, vol 1094. Springer, Singapore. https://doi.org/10.1007/978-981-15-1078-6_3
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DOI: https://doi.org/10.1007/978-981-15-1078-6_3
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