Optimizing crowd simulation based on real video data

Tracking of individuals and groups in video is an active topic of research in image processing and analyzing. This paper proposes an approach for the purpose of guiding a crowd simulation algorithm to mimic the trajectories of individuals in crowds as observed in real videos, which can be further used in image processing and computer vision research extensively. This is achieved by tuning the parameters used in the simulation automatically. It is required because the result of crowd simulation is very sensitive to the parameters. In our experiment, the simulation trajectories are generated by the RVO2 library and the real trajectories are extracted from the UCSD crowd video dataset. The Edit Distance on Real sequence (EDR) between the simulated and real trajectories are calculated. A genetic algorithm is applied to find the parameters that minimize the distances. The experimental results demonstrate that the trajectory distances between simulation and reality are significantly reduced after tuning the parameters of the simulator.