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.

本研究侧重于人群运动的多相流特性。稳定性是人群的重要预警因素。为了评价新来行人的行为和人群的稳定性，建立了一种基于目的性的新型运动结构分析模型，用于描述行人追求自己目标的连续性。我们使用目的地驱动分析方法用自驱动粒子表示人群。这些自驱动粒子是从人体检测到的可追踪特征点。然后我们使用轨迹来计算这些自驱动粒子的目的性，并选择目的性高的轨迹来估计群体的共同目的地和内在结构。最后，我们使用这些常见的目的地和人群结构来评估新到达行人的行为和人群稳定性。我们的研究表明，目的性参数是适合中等密度人群的描述符，并且所提出的目的地驱动分析方法能够表示复杂的人群运动行为。已经进行了使用人类和动物群体的合成和真实数据和视频的实验来验证所提出的方法。