使用主动Langevin模型理解人群流动
Understanding Crowd Flow Movements Using Active-Langevin Model
摘要：
人群流描述了人群的基本群体行为。了解这些运动背后的动力学可以帮助识别人群中的各种异常。然而，开发一个人群模型来描述这些流量是一项具有挑战性的任务。本文提出了一个基于物理的模型来描述密集人群中的运动。群体模型是基于活动朗之万方程，其中的运动点被假定为类似于流体中的活动胶体颗粒。该模型进一步增强了计算机视觉技术，在密集的人群中分割线性和非线性运动流。基于主动朗之万方程的人群分割的评估已经在公开的人群视频和我们自己的视频上完成。与现有的最先进的方法相比，所提出的方法能够以更小的光流误差和更好的精度分割流。
英文摘要：
Crowd flow describes the elementary group behavior of crowds. Understanding the dynamics behind these movements can help to identify various abnormalities in crowds. However, developing a crowd model describing these flows is a challenging task. In this paper, a physics-based model is proposed to describe the movements in dense crowds. The crowd model is based on active Langevin equation where the motion points are assumed to be similar to active colloidal particles in fluids. The model is further augmented with computer-vision techniques to segment both linear and non-linear motion flows in a dense crowd. The evaluation of the active Langevin equation-based crowd segmentation has been done on publicly available crowd videos and on our own videos. The proposed method is able to segment the flow with lesser optical flow error and better accuracy in comparison to existing state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
