Abstract
Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. For assessing a dataset complexity, we define a series of indicators around three concepts: Trajectory predictability; Trajectory regularity; Context complexity. We compare the most common datasets used in HTP in the light of these indicators and discuss what this may imply on benchmarking of HTP algorithms. Our source code is released on Github (https://github.com/crowdbotp/OpenTraj).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51, 4282–4286 (1995)
Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: Proceedings of the IEEE 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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (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: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 961–971 (2016)
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2255–2264 (2018)
Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data. arXiv preprint abs/2001.03093 (2020)
Amirian, J., Hayet, J.B., Pettré, J.: Social ways: learning multi-modal distributions of pedestrian trajectories with GANs. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2964–2972 (2019)
Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. Comput. Graph. Forum 26, 655–664 (2007)
Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11621–11631 (2020)
Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L., Eckstein, L.: The inD dataset: a drone dataset of naturalistic road user trajectories at German intersections. arXiv preprint abs/1911.07692 (2019)
Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory understanding in crowded scenes. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 549–565. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_33
Benfold, B., Reid, I.: Guiding visual surveillance by tracking human attention. In: Proceedings of the British Machine Vision Conference (BMVC) (2009)
Sun, P., et al.: Scalability in perception for autonomous driving: Waymo open dataset. arXiv preprint abs/1912.04838 (2019)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32, 1231–1237 (2013)
Yang, D., Li, L., Redmill, K., Ozguner, U.: Top-view trajectories: a pedestrian dataset of vehicle-crowd interaction from controlled experiments and crowded campus. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp. 899–904 (2019)
Ess, A., Leibe, B., Van Gool, L.: Depth and appearance for mobile scene analysis. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)
Leal-Taixé, L., Milan, A., Reid, I., Roth, S., Schindler, K.: MOTChallenge 2015: towards a benchmark for multi-target tracking. arXiv preprint abs/1504.01942 (2015)
Ferryman, J., Shahrokni, A.: PETS 2009: dataset and challenge. In: Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp. 1–6 (2009)
Sadeghian, A., Kosaraju, V., Gupta, A., Savarese, S., Alahi, A.: TrajNet: towards a benchmark for human trajectory prediction. arXiv preprint abs/1805.07663 (2018)
Chavdarova, T., et al.: WILDTRACK: a multi-camera HD dataset for dense unscripted pedestrian detection. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5030–5039 (2018)
Majecka, B.: Statistical models of pedestrian behaviour in the forum. Master’s thesis, School of Informatics, University of Edinburgh (2009)
Brscic, D., Kanda, T., Ikeda, T., Miyashita, T.: Person position and body direction tracking in large public spaces using 3D range sensors. IEEE Trans. Hum.-Mach. Syst. 43, 522–534 (2013)
Seyfried, A., Passon, O., Steffen, B., Boltes, M., Rupprecht, T., Klingsch, W.: New insights into pedestrian flow through bottlenecks. Transp. Sci. 43, 395–406 (2009)
Oh, S., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3153–3160 (2011)
Harmon, M., Lucey, P., Klabjan, D.: Predicting shot making in basketball learnt from adversarial multiagent trajectories. arXiv preprint abs/1609.04849 (2016)
Strigel, E., Meissner, D., Seeliger, F., Wilking, B., Dietmayer, K.: The Ko-PER intersection laser scanner and video dataset. In: Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1900–1901 (2014)
Bieshaar, M., Zernetsch, S., Hubert, A., Sick, B., Doll, K.: Cooperative starting movement detection of cyclists using convolutional neural networks and a boosted stacking ensemble. arXiv preprint abs/1803.03487 (2018)
Liang, J., Jiang, L., Murphy, K., Yu, T., Hauptmann, A.: The garden of forking paths: towards multi-future trajectory prediction. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 10508–10518 (2020)
Yan, Z., Duckett, T., Bellotto, N.: Online learning for human classification in 3D lidar-based tracking. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 864–871 (2017)
Chang, M.F., et al.: Argoverse: 3D tracking and forecasting with rich maps. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8748–8757 (2019)
Becker, S., Hug, R., Hübner, W., Arens, M.: An evaluation of trajectory prediction approaches and notes on the TrajNet benchmark. arXiv preprint abs/1805.07663 (2018)
Kothari, P., Kreiss, S., Alahi, A.: Human trajectory forecasting in crowds: a deep learning perspective (2020)
Ellis, D., Sommerlade, E., Reid, I.: Modelling pedestrian trajectory patterns with gaussian processes. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1229–1234 (2009)
Giuliari, F., Hasan, I., Cristani, M., Galasso, F.: Transformer networks for trajectory forecasting. arXiv preprint abs/2003.08111 (2020)
Claeskens, G., Hjort, N.L.: Cambridge series in statistical and probabilistic mathematics. In: The Bayesian Information Criterion, pp. 70–98. Cambridge University Press, Cambridge (2008)
Li, M., Westerholt, R., Fan, H., Zipf, A.: Assessing spatiotemporal predictability of LBSN: a case study of three foursquare datasets. GeoInformatica 22, 541–561 (2016)
Olivier, A.H., Marin, A., Crétual, A., Berthoz, A., Pettré, J.: Collision avoidance between two walkers: role-dependent strategies. Gait Posture 38, 751–756 (2013)
Karamouzas, I., Skinner, B., Guy, S.J.: Universal power law governing pedestrian interactions. Phys. Rev. Lett. 113, 238701 (2014)
Plaue, M., Chen, M., Bärwolff, G., Schwandt, H.: Trajectory extraction and density analysis of intersecting pedestrian flows from video recordings. In: Proceedings of the ISPRS Conference on Photogrammetric Image Analysis, pp. 285–296 (2011)
Acknowledgements
This research is supported by the CrowdBot H2020 EU Project http://crowdbot.eu and by the Intel Probabilistic Computing initiative. The work done by Francisco Valente Castro was sponsored using an MSc Scholarship given by CONACYT with the following scholar registry number 1000188.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Amirian, J., Zhang, B., Castro, F.V., Baldelomar, J.J., Hayet, JB., Pettré, J. (2021). OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12627. Springer, Cham. https://doi.org/10.1007/978-3-030-69544-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-69544-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69543-9
Online ISBN: 978-3-030-69544-6
eBook Packages: Computer ScienceComputer Science (R0)