## 4 Experiments
### Experimental Setup
These simulation-based datasets provide valuable insights into panic-induced deviations in trajectories, abnormal density fluctuations, and distress-related verbal cues, thereby supplementing real-world observations with a broader range of behavioral conditions. Table 4 summarizes the statistical distribution of the datasets used in this study, including the number of video sequences, average crowd density (pedestrian/m\({}^{2}\)), and annotated panic events. Because panic clips are markedly fewer than non-panic clips, we adopt three complementary strategies to counter class imbalance: (i) focal-loss re-weighting (\(\gamma=2\), \(\alpha=0.75\)) to down-weight easy negative samples while amplifying the contribution of hard, minority-class examples; (ii) a class-balanced sampler that oversamples video segments containing panic frames so that each mini-batch maintains an approximate 1:1 panic/non-panic ratio; and (iii) targeted data augmentation--random spatial cropping, horizontal flipping, and \(\pm\)10% temporal speed jitter--to increase the diversity of the limited panic samples. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Videos** & **Density (Pedestrians/m\({}^{2}\))** & **Panic Events** \\ \hline Stampede in Itaewon & 30 & 8.7 & 47 \\ \hline UCF Crowd & 150 & 2.8 & 24 \\ \hline Simulated Data & 160 & 4.0 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The statistical characteristics of related datasets. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Category** & **Parameter (Symbol)** & **Value/Range** & **Note** \\ \hline Arrival process & Arrival rate/\(\lambda\) & 0.8–1.2 ped/s & 47 \\ \hline Normal walking & Desired speed/\(V_{0}\) & 1.8 m/s & 24 \\ \hline Panic trigger & Density threshold & 4 ped/m\({}^{2}\) & 15 \\ \hline Panic walking & Desired speed/\(V_{p}\) & 2.8 m/s & Helbing social-force \\ \hline Rendering & Frame rate & 25 FPS & H.264 export \\ \hline Domain randomization & Speed noise & \(\pm\)10\% & Applied per agent \\ \hline Output & video clips & 160 & Final simulation dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key parameters of the SUMO–Unity simulation pipeline. The focal loss for a single sample is defined as  
\[\mathcal{L}_{focal}(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\,log(p_{t}) \tag{16}\]  
\[p_{t}=\left\{ \begin{array}{c}p,\;y=1\\ 1-p,\;y=0\end{array} \right. \tag{17}\]  
where \(p\) is the model's estimated probability for the positive (panic) class, \(\gamma\) is the focusing parameter that down-weights easy negatives, and \(\alpha_{t}=\alpha\) when \(y=1\) and \(1-\alpha\) otherwise. Ethical and Privacy Compliance  
All Itaewon-Stampedc clips in our dataset were retrieved exclusively from public domain sources--the Seoul Metropolitan Police Agency, Seoul Fire & Rescue Service, and three Korean national broadcasters (KBS, MBC, and YTN). Each video is released under a Creative Commons license (CC BY or CC BY-NC) or an equivalent public broadcast license that explicitly permits non-commercial academic reuse. Before analysis, every clip was fully de-identified: faces were blurred with a 20-pixel Gaussian kernel, and original speech tracks were removed. Because the resulting data contain no personally identifiable information, the study falls under the \"public data/non-identifiable\" exemption defined by Tongji University's Research Ethics Guideline and does not require additional Institutional Review Board approval or individual consent. The complete anonymization script and sample frame are provided in the project repository for transparency.