Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories

TL;DR

This paper introduces a novel approach using exemplar agent-based motion models to recognize crowd movement types from trajectories, outperforming existing methods and providing a new synthetic dataset for benchmarking.

Contribution

The paper proposes a new exemplar-AMM framework for crowd movement recognition and introduces SynCrowd, a synthetic dataset for training and benchmarking.

Findings

The exemplar-AMM method outperforms state-of-the-art in recognizing crowd movements.

The approach effectively filters noise and measures similarity to classify movements.

SynCrowd dataset provides a valuable resource for future crowd analysis research.

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.