Crowd modeling based on purposiveness and a destination-driven analysis method

Abstract

This study focuses on the multiphase flow properties of crowd motions. Stability is a crucial forewarning factor for the crowd. To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd, a novel motion structure analysis model is established based on purposiveness, and is used to describe the continuity of pedestrians’ pursuing their own goals. We represent the crowd with self-driven particles using a destination-driven analysis method. These self-driven particles are trackable feature points detected from human bodies. Then we use trajectories to calculate these self-driven particles’ purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd. Finally, we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability. Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds, and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors. Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.

摘要

本文主要研究人群运动的多相流特性。稳定性是人群的一个重要预警因素。为评价新到达行人的行为和人群的稳定性, 建立一种基于目的性的运动结构分析模型, 用于描述行人追求自身目标的连续性。使用目标驱动分析方法, 用自驱动粒子表示人群。这些自驱动粒子是人体图像的可跟踪特征点。然后, 利用轨迹计算这些自驱动粒子的目的性, 并选择高目的性轨迹估计公共目的地和人群内在结构。最后, 利用这些公共目的地和人群结构评估新到达行人的行为和人群稳定性。研究表明, 目的性参数是一个适于描述中等密度人群的描述符, 提出的目标驱动分析方法能够描述复杂人群运动行为。使用合成和真实的人类以及动物群体数据与视频, 验证了所提方法的有效性。