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Recent studies on crowd crush risk have shown that crowd density can be used as a key indicator of dangerous conditions.When density approaches around eight persons per square meter, crowd pressure waves begin to emerge and involuntary physical contact increases.
As density exceeds ten persons per square meter, severe overcrowding occurs, leading to a critical loss of mobility.
Based on these findings, this study adopts a deep learning–based approach using the P2PNet algorithm to estimate crowd density and extract spatial key points from CCTV footage, which serve as the foundation for subsequent crowd pressure analysis.
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IEEE/ICACT20260308 Slide.04
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