Abstract
The individual movement of pedestrians and their body parts, as for example shoulders, is of great interest to understand body movement and interactions and thus to improve pedestrian models. Nearly all laboratory experiments in pedestrian dynamics use camera data to obtain trajectories. A perpendicular top view of the camera does not only allow to extract the head position but also data of upper body segments. The detection is more reliable if shoulders are tagged with markers and for low densities of people. In this study a head-shoulder model is used to assign coloured shoulder markers to a person. The location of a marker is predicted by taking head position, basic body dimensions, movement direction and camera angle into account. It is implemented as a new feature in the software PeTrack. This paper shows a comparison of shoulder rotation measurements obtained from 3D motion capturing systems (Xsens) with those from camera data using the newly introduced model and detection technique. Detection rates and limits of the camera-based rotation measurement are shown and implications are given for the future application at high densities in crowds.
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Boltes et al.: PeTrack (Version TGF2022) (2022) doi: https://doi.org/10.5281/zenodo.7426553
Boltes et al.: PeTrack (2022) doi: https://doi.org/10.5281/zenodo.6320753
Boltes et al.: A hybrid tracking system of full-body motion inside crowds. Sensors (2021) doi: https://doi.org/10.3390/s21062108
Boltes, M. and Seyfried, A.: Collecting pedestrian trajectories. Neurocomputing (2013) doi: https://doi.org/10.1016/j.neucom.2012.01.036
Boomers et al.: Pedestrian Crowd Management Experiments: A Data Guidance Paper. Collective Dynamics. (submitted), doi: https://doi.org/10.48550/arXiv.2303.02319
Forschungszentrum Jülich, Institute for Advanced Simulation: Data Archive of Experiments on Pedestrian Dynamics. http://ped.fz-juelich.de/da
Jurgens et al.: International Anthropometric Data for Wok-Place and Machinary Design. In: Arbeitswissenschaftliche Erkenntnisse, Forschungsergebnisse für die Praxis, Vol. 108, pp 1–12. Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Dortmund, Germany (1998)
Romero-Ramirez et al.: Speeded up detection of squared fiducial markers. Image and Vision Computing (2018) doi: https://doi.org/10.1016/j.imavis.2018.05.004
Schepers et al.: Xsens MVN : consistent tracking of human motion using inertial sensing, Xsens Technologies Technical Report. Xsens Technologies (2018) https://www.researchgate.net/publication/324007368 . Cited 15 Mar 2022
Woodman, O.J.: An introduction to inertial navigation. University of Cambridge, Computer Laboratory (2007) doi: https://doi.org/10.48456/tr-696
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Boomers, A.K., Boltes, M. (2024). Shoulder Rotation Measurement in Camera and 3D Motion Capturing Data. In: Rao, K.R., Seyfried , A., Schadschneider, A. (eds) Traffic and Granular Flow '22 . TGF 2022. Lecture Notes in Civil Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-99-7976-9_5
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DOI: https://doi.org/10.1007/978-981-99-7976-9_5
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