Physics-Environment Interaction Network for Dense Crowd Behavior Recognition
Physics-Environment Interaction Network for Dense Crowd Behavior Recognition
Physics-Environment Interaction Network for Dense Crowd Behavior Recognition
In high-density public scenarios such as religious gatherings, concerts, sports arenas, and urban transit hubs, understanding and recognizing crowd behavior is critical to ensuring safety, optimizing infrastructure, and preventing disasters like stampedes. Traditional computer vision approaches often struggle in dense crowd conditions due to heavy occlusion and complex human-environment interactions. To address these challenges, we introduce a novel Physics-Environment Interaction Network (PEIN) — a deep learning framework inspired by physical dynamics and environmental context to accurately recognize behaviors in dense crowds.
The PEIN model integrates principles from crowd physics (e.g., social force models, repulsion-attraction dynamics) with scene-aware environmental perception, enabling a more robust and interpretable representation of human movement in constrained spaces. Unlike conventional models that focus solely on visual features or trajectory-based cues, PEIN emphasizes how individuals interact with surrounding agents (people, obstacles, exits) and how environmental structures influence flow patterns. This multi-perspective fusion enables the model to discern abnormal crowd behavior, collective motion trends, and emergent panic with high accuracy.
By embedding a physics-inspired interaction graph within a spatiotemporal convolutional framework, PEIN captures both short-term motion cues and long-term environmental dependencies. The system is trained using annotated dense crowd datasets, and it generalizes well across varied real-world scenarios such as subway stations, public festivals, and surveillance footage. Additionally, the environmental component of the model allows it to adapt to changing layouts and obstacles, making it ideal for deployment in dynamic urban environments.
The proposed approach not only enhances the recognition of activities like walking, queueing, dispersing, and panic-induced motion, but also paves the way for real-time predictive modeling and alert systems in crowd management applications. Future extensions may integrate drone-based aerial imaging and multimodal sensory data for even richer environmental representation.
Ultimately, the Physics-Environment Interaction Network bridges the gap between human behavioral science and computational intelligence, enabling smarter, safer, and more responsive systems in the age of urban mobility and public safety.
Global Particle Physics Excellence Awards
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