Graphic - Graph-Based Representation for Analyzing People’s High-Level Interactions in Crowds

Abstract

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The need for automated systems to aid law enforcement during densely packed events arises from the inherent danger of large crowds, evidenced by historical instances of stampedes and crushes. Existing methods vary from basic crowd statistics extraction to detailed anomaly detection in behavior classification, but often focus on single, pre-segmented scenes. Our work addresses classifying crowd behaviors in environments where multiple behaviors coexist within a single scene, defined as a multi-class crowd motion characterization challenge. We use a microscopic approach for scenes captured by drones at varying altitudes, without prior manipulation. This approach combines graph-based representations of individuals and flow images, facilitating classification of diverse crowd behaviors in unsegmented scenes. Tested on a public dataset, our method shows promising results in analyzing complex crowd dynamics.