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To calibrate the model, we applied our deep learning framework to real disaster cases, including the 2021 Meron and 2022 Seoul Halloween events.
Using P2PNet, head-level spatial coordinates were extracted from CCTV footage, providing key parameters such as inter-personal distance, cluster compactness, and local density gradients.
By comparing these cases, we identified a critical transition threshold at approximately nine to ten persons per square meter, just before physical crowd collapse.
These results confirm that the proposed CPI can function as an early warning indicator for dangerous crowd conditions.
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IEEE/ICACT20260308 Slide.14
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