Extracting macroscopic quantities in crowd behaviour with deep learning

Abstract

Abnormal behaviours in crowded populations can pose significant threats to public safety, with the occurrence of such anomalies often corresponding to changes in macroscopic quantities of the complex system. Therefore, the automatic extraction and prediction of macroscopic quantities in pedestrian collective behaviour becomes significant. In this study, we generated pedestrian evacuation data through simulation, and calculated the average kinetic energy, entropy and order parameter of the system based on principles of statistical physics. These macroscopic quantities can characterize the changes in crowd behaviour patterns over time and can also assist in detecting abnormalities. Subsequently, we designed deep convolutional neural networks(CNNs) to estimate these macroscopic quantities directly from frame-by-frame image data. In the end, a convolutional auto-encoder(CAE) model is trained to learn the underlying physics unsupervisedly. Successful results indicate that deep learning methods can directly extract macroscopic information from crowd dynamics, aiding in analysing collective behaviour.