  In this paper, we present a data-driven approach to generate realistic
steering behaviors for virtual crowds in crowd simulation. We take advantage of
both rule-based models and data-driven models by applying the interaction
patterns discovered from crowd videos. Unlike existing example-based models in
which current states are matched to states extracted from crowd videos
directly, our approach adopts a hierarchical mechanism to generate the steering
behaviors of agents. First, each agent is classified into one of the
interaction patterns that are automatically discovered from crowd video before
simulation. Then the most matched action is selected from the associated
interaction pattern to generate the steering behaviors of the agent. By doing
so, agents can avoid performing a simple state matching as in the traditional
example-based approaches, and can perform a wider variety of steering behaviors
as well as mimic the cognitive process of pedestrians. Simulation results on
scenarios with different crowd densities and main motion directions demonstrate
that our approach performs better than two state-of-the-art simulation models,
in terms of prediction accuracy. Besides, our approach is efficient enough to
run at interactive rates in real time simulation.
