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This paper presents a novel approach to simulate virtual human crowds in emergency situations. Our model is based on two previous works, on a physical model proposed by Helbing, where individuals are represented by a particle system affected by "social forces" that impels them to go to a point-objective, while avoiding collisions with obstacles and other agents. As a new property, the virtual agents are endowed with different attributes and individualities as proposed by Braun et al. The main contributions of this paper are the treatment of complex environments and their implications on agents' movement, the management of alarms distributed in space, the virtual agents endowed with perception of emergency events and their consequent reaction as well as changes in their individualities. The prototype reads a XML file where different scenarios can be simulated, such as the characteristics of population, the virtual scene description, the alarm configuration and the properties of hazardous events. As output, the prototype generates information in order to measure the impact of parameters on saved, injured and dead agents. In addition, some results and validation are discussed.

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In this paper, we propose a new model to simulate the movement of virtual humans based on trajectories captured automatically from filmed video sequences. These trajectories are grouped into similar classes using an unsupervised clustering algorithm, and an extrapolated velocity field is generated for each class. A physically-based simulator is then used to animate virtual humans, aiming to reproduce the trajectories fed to the algorithm and at the same time avoiding collisions with other agents. The proposed approach provides an automatic way to reproduce the motion of real people in a virtual environment, allowing the user to change the number of simulated agents while keeping the same goals observed in the filmed video.

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