  In this paper, we present a novel method to recognize the types of crowd
movement from crowd trajectories using agent-based motion models (AMMs). Our
idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe
the crowd movement. Specifically, we propose an optimization framework that
filters out the unknown noise in the crowd trajectories and measures their
similarity to the exemplar-AMMs to produce a crowd motion feature. We then
address our real-world crowd movement recognition problem as a multi-label
classification problem. Our experiments show that the proposed feature
outperforms the state-of-the-art methods in recognizing both simulated and
real-world crowd movements from their trajectories. Finally, we have created a
synthetic dataset, SynCrowd, which contains 2D crowd trajectories in various
scenarios, generated by various crowd simulators. This dataset can serve as a
training set or benchmark for crowd analysis work.
