Abstract
Crowd motion data is fundamental for understanding and simulating realistic crowd behaviours. Such data is usually collected through controlled experiments to ensure that both desired individual interactions and collective behaviours can be observed. It is however scarce, due to ethical concerns and logistical difficulties involved in its gathering, and only covers a few typical crowd scenarios. In this work, we propose and evaluate a novel Virtual Reality based approach lifting the limitations of real-world experiments for the acquisition of crowd motion data. Our approach immerses a single user in virtual scenarios where he/she successively acts each crowd member. By recording the past trajectories and body movements of the user, and displaying them on virtual characters, the user progressively builds the overall crowd behaviour by him/herself. We validate the feasibility of our approach by replicating three real experiments, and compare both the resulting emergent phenomena and the individual interactions to existing real datasets. Our results suggest that realistic collective behaviours can naturally emerge from virtual crowd data generated using our approach, even though the variety in behaviours is lower than in real situations. These results provide valuable insights to the building of virtual crowd experiences, and reveal key directions for further improvements.
Highlights
Crowd datasets, i.e., recordings of trajectories from numerous people moving together in a same location, are paramount in the understanding, modelling and simulation of crowd behaviours
The goal of this study is to investigate whether Virtual Reality (VR) can be used to acquire novel crowd motion data in an unprecedented manner, that we call the one-man-crowd paradigm, where a single user immersed in a virtual environment either successively acts all the characters of the crowd (S2N) or different characters in different crowds (N2N)
We explored the use of VR to overcome cost and logistics concerns raised by real crowd experiments, which are paramount in the crowd simulation community, and proposed the one-man-crowd paradigm (OMC) paradigm
Summary
I.e., recordings of trajectories from numerous people moving together in a same location, are paramount in the understanding, modelling and simulation of crowd behaviours. These datasets are used to study and understand collective behaviours, or to train, calibrate and evaluate simulation models. Such datasets remain rare in spite of the large interest they yield. This scarcity is due to various reasons, such as costs, logistical, ethical and technical issues. The current lack of valuable crowd datasets is significantly hampering research on understanding and simulating collective behaviours
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