Author: Solenne Fortun

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Video: How can we model humans?

Discover crowd modelling with our new video! 🎥 Have you always wanted to understand how it’s possible to move from reality to the virtual world? Dan Casas, Marc Comino, Melania Prieto Martin and Gonzalo Gomez from Universidad Rey Juan Carlos explain how they model humans as part of the CrowdDNA project.

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D3.2: ML Detector to analyse crowd movements in videos

This deliverable describes the efforts done during Period 1, Period 2 and part of Period 3 in the Work Package 3 of the CrowdDNA project towards developing a new crowd simulator algorithm tailored to model both macro and micro-level crowd characteristics.

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D3.1: ML Generator to analyse crowd movements in videos

This deliverable describes the efforts done during periods 1, 2 and half-way through 3 in the Work Package 3 (WP3) of the project towards, first, generating synthetic data and, second, training a detector.

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[Under review] PED 2024: Standing Balance Recovery Strategies of Young Adults in a Densely Populated Environment Following External Perturbations

T. Chatagnon, S. Feldmann, J. Adrian, A.-H. Olivier, C. Pontonnier, L. Hoyet and J. Pettre

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[Under review] PED 2024: Temporal segmentation of motion propagation in response to an external impulse

S. Feldmann, T. Chatagnon, J. Adrian, J. Pettre and A. Seyfried

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D1.3: Field dataset

This report details the work completed to achieve deliverable D1.3 Field Data and outlines the data captured, analysis and sharing of data and the interrelation of this deliverable with other consortium partners and deliverables.