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• We propose an augmented crowd simulation system using automatic determination and reconstruction of navigable areas in static, surveillancealike videos. • We utilize pedestrian trajectory data and use deep learning-based semantic segmentation methods to identify navigable areas. • We simulate artificial agents over the reconstructed navigable area together with real agents in the video via collision avoidance. • We demonstrate the accuracy and applicability of the proposed navigable area reconstruction approach on various crowded outdoor scenarios. [Display omitted] 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. [ABSTRACT FROM AUTHOR]

We present an online parameter learning algorithm for data-driven crowd simulation and crowd content generation. Our formulation is based on incrementally learning pedestrian motion models and behaviors from crowd videos. We combine the learned crowd-simulation model with an online tracker to compute accurate, smooth pedestrian trajectories. We refine the motion model using an optimization technique to estimate the agents׳ simulation parameters. We also use an adaptive-particle filtering scheme for improved computational efficiency. We highlight the benefits of our approach for improved data-driven crowd simulation, including crowd replication, augmented crowds and merging the behavior of pedestrians from multiple videos. We highlight our algorithm׳s performance in various test scenarios containing tens of human-like agents and evaluate it using standard metrics. [ABSTRACT FROM AUTHOR]