Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories
This work addresses crowd movement recognition for applications in surveillance and safety, but it is incremental as it builds on existing agent-based motion models.
The paper tackles the problem of recognizing crowd movement types from pedestrian trajectories by introducing exemplar-AMMs and an optimization framework to filter noise and extract features, achieving state-of-the-art performance on both simulated and real-world data.
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.