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arXiv · 1603.09454

Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories

Wenxi Liu·Rynson W. H. Lau·Xiaogang Wang·Dinesh Manocha
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Abstract

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.

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Wenxi Liu, Rynson W. H. Lau, Xiaogang Wang, Dinesh Manocha. 2016-03-31. Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories. https://doi.org/10.1109/tmm.2016.2598091

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