Abstract
Pedestrians have an intuitive ability for navigation to avoid obstacles and nearby pedestrians. If we want to predict future positions of a pedestrian, we should know how the pedestrian adjust his direction to avoid collisions. In this work, we present a simple and effective framework for human trajectory prediction to generate the future sequence based on pedestrian past positions. The method, called Collision-Free LSTM, extends the classical LSTM by adding Repulsion pooling layer to share hidden-states of neighboring pedestrians. The model can learn both the temporal information of trajectories and the interactions between pedestrians, which is in contrast to traditional methods using hand-crafted features such as Social forces. The experiments results on two public datasets show that our model can achieve state-of-the-art performance with assessment metrics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 330–339. ACM (2007)
Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604. ACM (2007)
Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: experimental studies and comparative evaluation. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 312–319. IEEE (2009)
Ballan, L., Castaldo, F., Alahi, A., Palmieri, F., Savarese, S.: Knowledge transfer for scene-specific motion prediction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 697–713. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_42
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)
Koppula, H.S., Saxena, A.: Anticipating human activities using object affordances for reactive robotic response. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 14–29 (2016)
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
Choi, W., Savarese, S.: A unified framework for multi-target tracking and collective activity recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 215–230. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_16
Choi, W., Savarese, S.: Understanding collective activities of people from videos. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1242–1257 (2014)
Leal-Taixe, 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)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 935–942. IEEE (2009)
Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg, T.L.: Who are you with and where are you going? In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1345–1352. IEEE (2011)
Alahi, A., Ramanathan, V., Fei-Fei, L.: Socially-aware large-scale crowd fore-casting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2203–2210 (2014)
Yi, S., Li, H., Wang, X.: Understanding pedestrian behaviors from stationary crowd groups. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3488–3496 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
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)
Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: 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)
Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer-Graphics Forum, vol. 26, pp. 655–664. Wiley Online Library (2007)
Luber, M., Stork, J.A., Tipaldi, G.D., Arras, K.O.: People tracking with human motion predictions from social forces. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 464–469. IEEE (2010)
Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 1928–1935. IEEE (2008)
Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998)
Tordeux, A., Chraibi, M., Seyfried, A.: Collision-free speed model for pedestrian dynamics. In: Knoop, V., Daamen, W. (eds.) Traffic and Granular Flow 2015, pp. 225–232. Springer, Cham (2016)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Dauphin, Y., de Vries, H., Bengio, Y.: Equilibrated adaptive learning rates for non-convex optimization. In: Advances in Neural Information Processing Systems, pp. 1504–1512 (2015)
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Savannah, Georgia, USA (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Xu, K., Qin, Z., Wang, G., Huang, K., Ye, S., Zhang, H. (2018). Collision-Free LSTM for Human Trajectory Prediction. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-73603-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73602-0
Online ISBN: 978-3-319-73603-7
eBook Packages: Computer ScienceComputer Science (R0)