Virtual crowds: An LSTM-based framework for crowd simulation

Social modeling of pedestrian dynamics is a key element to understand the behavior of crowded scenes. Existing crowd models like the Social Force Model and the Reciprocal Velocity Obstacle, traditionally rely on empirically-defined functions to characterize the dynamics of a crowd. On the other hand, frameworks based on deep learning, like the Social LSTM and the Social GAN, have proven their ability to predict pedestrians trajectories without requiring a predefined mathematical model. In this paper we propose a new paradigm for crowd simulation based on a pool of LSTM networks. Each pedestrian is able to move independently and interact with the surrounding environment, given a starting point and a destination goal.