Crowd 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.

人群模拟可模仿不同环境中个人的群体动态。娱乐，安全和教育领域的应用需要将模拟人群增加到真实人的视频中。在这种情况下，虚拟代理应与视频中的环境和人们进行现实的互动。扩充任务的一个组成部分是确定视频中的可导航区域。在这项工作中，我们利用语义分割和行人检测来自动定位和重建类似监视视频的可导航区域。我们将生成的平面网格放入3D人群仿真环境中，以集成在视频内部导航的虚拟代理，从而避免与真实的行人和其他虚拟代理发生冲突。我们使用实时监控视频报告我们的开源系统的性能，基于自动确定的导航区域和摄像机配置的准确性。我们证明了我们的系统会生成准确的可导航区域，以进行现实的增强人群模拟。