## 3 Methodology
### Fusion-Based Multi-Feature Method for Pedestrian Panic Recognition  
To effectively unify these heterogeneous feature streams, we design a lightweight attention-enhanced MLP fusion module, which adaptively aggregates spatial, temporal, and semantic cues into a compact joint representation for final panic recognition. The proposed framework integrates three complementary feature streams: spatial crowd density distributions, temporal pedestrian trajectories, and semantic verbal cues.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**NO.** & **Event Type** & **Key Word** & **Turnout** & **Weight** & **NO.** & **Event Type** & **Key Word** & **Turnout** & **Weight** \\ \hline
1 & Medical accident & Murder & 152 & 0.51 & 2 & Stampede & Let me out & 13 & 0.04 \\ \hline
3 & Medical accident & Stabbing & 76 & 0.25 & 4 & Stampede & Don’t push me & 32 & 0.11 \\ \hline
5 & Medical accident & Help & 21 & 0.07 & 6 & Stampede & Someone fell & 57 & 0.19 \\ \hline
7 & Medical accident & Pay with your life & 38 & 0.13 & 8 & Stampede & Trampled to death & 73 & 0.24 \\ \hline
9 & Medical accident & Black-hearted & 2 & 0.01 & 10 & Stampede & Crushed to death & 53 & 0.18 \\ \hline
11 & Medical accident &
\begin{tabular}{c} Disregard for \\ human life \\ \end{tabular} & 7 & 0.02 & 12 & Stampede & Can’t breathe & 50 & 0.17 \\ \hline
13 & Medical accident & Misdiagnosis & 4 & 0.01 & 14 & Stampede & Help & 22 & 0.07 \\ \hline
15 & Disaster event & Landside & 32 & 0.11 & 16 & Terrorist attack & Kidnapping & 42 & 0.14 \\ \hline
17 & Disaster event & Earthquake & 57 & 0.19 & 18 & Terrorist attack & Explosion & 36 & 0.12 \\ \hline
19 & Disaster event & Fire & 28 & 0.09 & 20 & Terrorist attack & Bomb & 48 & 0.16 \\ \hline
21 & Disaster event & Mudslide & 23 & 0.08 & 22 & Terrorist attack & Gun & 47 & 0.16 \\ \hline
23 & Disaster event & Flood & 45 & 0.15 & 24 & Terrorist attack & Poison gas & 35 & 0.11 \\ \hline
25 & Disaster event & Tornado & 42 & 0.14 & 26 & Terrorist attack & Dead body & 26 & 0.09 \\ \hline
27 & Disaster event & Tsunami & 73 & 0.24 & 28 & Terrorist attack & Murder & 66 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical table of key segment weights of the semantic recognition criterion.  
Each modality is processed independently to extract a 512-dimensional high-level embedding, denoted as \(x_{i}\) where \(i\in\left\{\mathit{density},\mathit{trajectory},\mathit{semantic}\right\}\).  
To achieve effective fusion, each modality-specific embedding is first projected into a shared latent space using a fully connected layer with ReLU activation:  
\[z_{i}=ReLU(W_{i}x_{i}),\ \ W_{i}\in R^{256\times 512} \tag{13}\]  
A learnable attention query vector \(u\in R^{256}\) is introduced to adaptively weight the three modalities. The attention weights are computed as:  
\[w_{i}=\left(u^{\top}z_{i}\right),\ \ \sum_{i}w_{i}=1 \tag{14}\]  
and the fused representation is obtained as:  
\[z_{\mathit{fuse}}=\sum_{i}w_{i}z_{i}\in R^{256} \tag{15}\]  
The fused feature \(z_{\mathit{fuse}}\) is further refined by a multi-layer perceptron (MLP) with hidden dimensions of 128 and 32, using GELU activation and a dropout rate of 0.2 to prevent overfitting. Finally, a sigmoid activation outputs the frame-level panic probabilities.  
The proposed attention-MLP fusion module introduces only 0.13 million parameters and adds less than 1 ms of inference overhead on an RTX 3080 GPU. Ablation experiments demonstrate that replacing the attention-MLP with naive early concatenation reduces the F1 score on the Itaewon dataset from 0.882 to 0.811, highlighting the importance of the designed fusion strategy. The detailed workflow is illustrated in Figure 6.  
Figure 6: Pedestrian panic behavior recognition workflow.