In this paper we present a data-driven approach for fitting behaviors to simulated pedestrian crowds. Our method annotates agent trajectories, generated by any crowd simulator, with action-tags. The aggregate effect of animating the agents according to the tagged trajectories enhances the impression that the agents are interacting with one another and with the environment. In a preprocessing stage, the stimuli which motivated a person to perform an action, as observed in a crowd video, are encoded into examples. Using the examples, non-linear, action specific influence functions are encoded into two-dimensional maps which evaluate, for each action, the relative importance of a stimulus within a configuration. At run time, given an agents stimuli configuration, the importance of each stimulus is determined and compared to the examples. Thus, the probability of performing each action is approximated and an action-tag is chosen accordingly. We fit behaviors to pedestrian crowds, thereby enhancing their natural appearance.
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