Benchmarking High-Fidelity Pedestrian Tracking Systems for Research, Real-Time Monitoring and Crowd Control

Caspar Pouw; Joris Willems; Frank van Schadewijk; Jasmin Thurau; Federico Toschi; Alessandro Corbetta

doi:10.17815/CD.2021.134

Authors

Caspar Pouw Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands and ProRail Stations, Utrecht, The Netherlands
Joris Willems Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
Frank van Schadewijk ProRail Stations, Utrecht, The Netherlands
Jasmin Thurau SBB AG, Bern, Switzerland
Federico Toschi Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands and CNR-IAC, Rome, Italy
Alessandro Corbetta Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands

DOI:

https://doi.org/10.17815/CD.2021.134

Keywords:

high-fidelity pedestrian tracking, sensor benchmarking, crowd monitoring, real-life pedestrian measurements, industrial and societal applications

Abstract

High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research allowing to quantify statistics of relevant observables including walking velocities, mutual distances and body orientations. As this technology advances, it is becoming increasingly useful also in society. In fact, continued urbanization is overwhelming existing pedestrian infrastructures such as transportation hubs and stations, generating an urgent need for real-time highly-accurate usage data, aiming both at flow monitoring and dynamics understanding. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. This is not only necessary to guarantee data quality, but also to identify systematic errors. Currently, there is no established policy in this context. In this contribution, we present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques. The suite is technology-independent and it is applicable to academic and commercial pedestrian tracking systems, operating both in lab environments and real-life conditions. The benchmark suite consists of 5 tests addressing specific aspects of pedestrian tracking quality, including accurate line-based crowd flux estimation, local density estimation, individual position detection and trajectory accuracy. The output of the tests are quality factors expressed as single numbers. We provide the benchmark results for two tracking systems, both operating in real-life, one commercial, and the other based on overhead depth-maps developed at TU Eindhoven, within the Crowdflow topical group. We discuss the results on the basis of the quality factors and report on the typical sensor and algorithmic performance. This enables us to highlight the current state-of-the-art, its limitations and provide installation recommendations, with specific attention to multi-sensor setups and data stitching.

Author Biography

Alessandro Corbetta, Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands

Department of Applied Physics
Eindhoven University of Technology

References

Eringa, P.: Prorail: Meer en snellere treinen. https://www.prorail.nl/nieuws/meer-en-snellere-treinen (2020). Accessed: 2021-07-28

Daamen, W., Hoogendoorn, S.P.: Experimental research of pedestrian walking behavior. Transportation Research Record 1828(1), 20-30 (2003). doi:10.3141/1828-03

Seyfried, A., Steffen, B., Klingsch, W., Boltes, M.: The fundamental diagram of pedestrian movement revisited. Journal of Statistical Mechanics: Theory and Experiment 2005(10), 10002 (2005). doi:10.1088/1742-5468/2005/10/P10002

Kretz, T., Grünebohm, A., Kaufman, M., Mazur, F., Schreckenberg, M.: Experimental study of pedestrian counterflow in a corridor. Journal of Statistical Mechanics: Theory and Experiment 2006(10), P10001 (2006). doi:10.1088/1742-5468/2006/10/P10001

Moussad, M., Helbing, D., Garnier, S., Johansson, A., Combe, M., Theraulaz, G.: Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proceedings of the Royal Society B: Biological Sciences 276, 2755-2762 (2009). doi:10.1098/RSPB.2009.0405

Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Evacuation Dynamics: Empirical Results, Modeling and Applications. In: Extreme Environmental Events, pp. 517-550. Springer, New York, NY (2011). doi:10.1007/978-1-4419-7695-6_29

Seitz, M.J., Köster, G.: Natural discretization of pedestrian movement in continuous space. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 86(4) (2012). doi:10.1103/PhysRevE.86.046108

Yamamoto, H., Yanagisawa, D., Feliciani, C., Nishinari, K.: Body-rotation behavior of pedestrians for collision avoidance in passing and cross flow. Transportation Research Part B: Methodological 122, 486-510 (2019). doi:10.1016/j.trb.2019.03.008

Corbetta, A., Bruno, L., Muntean, A., Toschi, F.: High statistics measurements of pedestrian dynamics. Transportation Research Procedia 2, 96-104 (2014). doi:10.1016/J.TRPRO.2014.09.013

Brščić, D., Zanlungo, F., Kanda, T.: Density and velocity patterns during one year of pedestrian tracking. In: Transportation Research Procedia, vol. 2, pp. 77-86 (2014). doi:10.1016/j.trpro.2014.09.011

Zanlungo, F., Yücel, Z., Brščić, D., Kanda, T., Hagita, N.: Intrinsic group behaviour: Dependence of pedestrian dyad dynamics on principal social and personal features. PLoS ONE 12(11) (2017). doi:10.1371/journal.pone.0187253

Corbetta, A., Meeusen, J.A., Lee, C., Benzi, R., Toschi, F.: Physics-based modeling and data representation of pairwise interactions among pedestrians. Physical Review E 98(6) (2018). doi:10.1103/PhysRevE.98.062310

Willems, J., Corbetta, A., Menkovski, V., Toschi, F.: Pedestrian orientation dynamics from high-fidelity measurements. Scientific Reports 2020 10:1 10(1), 1-10 (2020). doi:10.1038/s41598-020-68287-6

Pouw, C.A.S., Toschi, F., van Schadewijk, F., Corbetta, A.: Monitoring physical distancing for crowd management: Real-time trajectory and group analysis. PLOS ONE 15 (2020). doi:10.1371/journal.pone.0240963

Boltes, M., Seyfried, A.: Collecting pedestrian trajectories. Neurocomputing 100, 127-133 (2013). doi:10.1016/J.NEUCOM.2012.01.036

Brščić, D., Kanda, T., Ikeda, T., Miyashita, T.: Person tracking in large public spaces using 3-D range sensors. IEEE Transactions on Human-Machine Systems 43(6), 522-534 (2013). doi:10.1109/THMS.2013.2283945

Seer, S., Brändle, N., Ratti, C.: Kinects and human kinetics: A new approach for studying pedestrian behavior. Transportation Research Part C: Emerging Technologies 48, 212-228 (2014). doi:10.1016/j.trc.2014.08.012

Yoshimura, Y., Sobolevsky, S., Ratti, C., Girardin, F., Carrascal, J.P., Blat, J., Sinatra, R.: An analysis of visitors' behavior in the louvre museum: A study using bluetooth data. Environment and Planning B: Planning and Design 41(6), 1113-1131 (2014). doi:10.1068/b130047p

Centorrino, P., Corbetta, A., Cristiani, E., Onofri, E.: Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization. Journal of Computational Science 53, 101357 (2021). doi:10.1016/j.jocs.2021.101357

Hong, H., De Silva, G.D., Chan, M.C.: Crowdprobe: Non-invasive crowd monitoring with wifi probe. In: Proceedings of the ACM Interactactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, p. 23. Association for Computing Machinery, New York, NY, USA (2018). doi:10.1145/3264925

Georgievska, S., Rutten, P., Amoraal, J., Ranguelova, E., Bakhshi, R., de Vries, B.L., Lees, M., Klous, S.: Detecting high indoor crowd density with wi-fi localization: a statistical mechanics approach. Journal of Big Data 6 (2019). doi:10.1186/S40537-019-0194-3

van den Heuvel, J., Thurau, J., Mendelin, M., Schakenbos, R., van Ofwegen, M., Hoogendoorn, S.P.: An application of new pedestrian tracking sensors for evaluating platform safety risks at swiss and dutch train stations. In: Hamdar, S.H. (ed.) Traffic and Granular Flow '17, pp. 277-286. Springer International Publishing (2019). doi:10.1007/978-3-030-11440-4_42

Thurau, J., van den Heuvel, J., Keusen, N., van Ofwegen, M., Hoogendoorn, S.P.: Influence of Pedestrian Density on the Use of the Danger Zone at Platforms of Train Stations. In: Traffic and Granular Flow '17, pp. 287-296. Springer International Publishing (2019). doi:10.1007/978-3-030-11440-4_32

Thurau, J., Keusen, N.: Influence of obstacles on the use of the danger zone on railway platforms. Collective Dynamics 5(0), A84 (2020). doi:10.17815/cd.2020.84

Microsoft Coorporation, Kinect for Xbox 360, Redmond WA, USA

Kroneman, W., Corbetta, A., Toschi, F.: Accurate pedestrian localization in overhead depth images via height-augmented hog. Collective Dynamics 5 (2020). doi:10.17815/CD.2020.30

Corbetta, A., Kroneman, W., Donners, M., Haans, A., Ross, P., Trouwborst, M., Van de Wijdeven, S., Hultermans, M., Sekulovski, D., Van der Heijden, F., Mentink, S., Toschi, F.: A large-scale real-life crowd steering experiment via arrow-like stimuli. Collective Dynamics 5(0), 61-68 (2020). doi:10.17815/cd.2020.34

Allan, D.B., Caswell, T., Keim, N.C., van der Wel, C.M., Verweij, R.W.: soft-matter/trackpy: Trackpy v0.5.0 (2021). doi:10.5281/zenodo.4682814

Corbetta, A., Lee, C., Benzi, R., Muntean, A., Toschi, F.: Fluctuations around mean walking behaviors in diluted pedestrian flows. Physical Review E 95(3) (2017)

Liu, X., Song, W., Zhang, J.: Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing. Physica A: Statistical Mechanics and its Applications 388(13), 2717-2726 (2009). doi:10.1016/j.physa.2009.03.017