Doğan, YalımSonlu, SinanGüdükbay, Uğur2022-02-012022-02-012021-040097-8493http://hdl.handle.net/11693/76956Crowd simulations imitate the group dynamics of individuals in different environments. Applications in entertainment, security, and education require augmenting simulated crowds into videos of real people. In such cases, virtual agents should realistically interact with the environment and the people in the video. One component of this augmentation task is determining the navigable regions in the video. In this work, we utilize semantic segmentation and pedestrian detection to automatically locate and reconstruct the navigable regions of surveillance-like videos. We place the resulting flat mesh into our 3D crowd simulation environment to integrate virtual agents that navigate inside the video avoiding collision with real pedestrians and other virtual agents. We report the performance of our open-source system using real-life surveillance videos, based on the accuracy of the automatically determined navigable regions and camera configuration. We show that our system generates accurate navigable regions for realistic augmented crowd simulations.EnglishPedestrian detection and trackingData-driven simulationThree-dimensional reconstructionCrowd simulationAugmented realityDeep learningArticle10.1016/j.cag.2021.01.012