Understanding Crowd Flow Movements Using Active-Langevin Model
This work addresses the challenge of understanding crowd dynamics for applications like anomaly detection, but it is incremental as it builds on existing physics-based and computer-vision techniques.
The authors tackled the problem of modeling dense crowd movements by proposing a physics-based active Langevin equation model, which segments linear and non-linear flows with reduced optical flow error and improved accuracy compared to existing methods.
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.