## 4 Experiments
### Case Analysis  
To evaluate the practical effectiveness of the proposed multimodal panic recognition system, a case study was conducted using real-world surveillance data from Stampede in Itaewon, which is a high-density transportation hub. The dataset includes video recordings of normal pedestrian movements and panic scenarios, allowing for a detailed assessment of the system's ability to detect and analyze abnormal behaviors in a complex micro-road network environment.  
The input video stream is first processed by CDNet, which generates a crowd density map. Subsequently, contour-based criteria are applied to identify panic regions based on predefined thresholds. Specifically, the contour quantity variation threshold \(\tau_{N}\) is set to 7, while the contour area variation threshold \(\tau_{A}\) is set to 30%. The overall panic score S is determined using a threshold of 2.2, where the weights for contour quantity change \((\omega_{1})\) and contour area change \((\omega_{2})\) are set to 0.6 and 0.4, respectively, reflecting the greater significance of contour quantity variation in the Itaewon, the results are illustrated in Figure 8, where (a) and (c) represent the judgment results of crowd count and panic area, respectively.  
Figure 8: Microscopic road network-based panic area identification via CDNet and contour analysis. (**a**) Results of crowd counting using CDNet, in which the predicted number of pedestrians is 313.1 and the actual number of pedestrians is 339. (**b**) Generated heatmap representing population density. (**c**) Identified panic areas based on contour analysis, where red area is the judged panic area.  
In addition to density-based panic region detection, pedestrian abnormal trajectory data are extracted according to the panic trajectory recognition criteria, which categorize anomalies such as counterflow movements, wandering behaviors, and abrupt directional changes. The extracted abnormal trajectories, including counterflow movements, behaviors, and other deviations from normal pedestrian flow, are processed by the LSTM- based. Trajectory analysis module to generate high-dimensional trajectory feature vectors. Simultaneously, speech and textual data derived from Baidu AI's semantic analysis are fed into the Transformer-based semantic representation module, which encodes panic-related linguistic features into structured feature vectors. These modality-specific feature embeddings are subsequently fused through a multi-layer perceptron (MLP)-based multi-modal feature fusion framework, where optimized weighting mechanisms are applied to integrate spatial, temporal, and semantic representations. The final panic probability score, which quantifies the likelihood of panic-induced behaviors, is obtained as the output of this fusion process, as illustrated in Figure 9.  
The results demonstrate that the proposed system successfully detects panic behaviors before large-scale crowd disturbances occur, allowing early intervention and risk mitigation. The multimodal integration of spatial, temporal, and semantic features enables a more comprehensive understanding of panic propagation, distinguishing true panic events from high-density but non-emergency crowd formations. The system's performance in the Stampede in Itaewon, South Korea, validates its robustness in real-world, high-density public settings, confirming its potential applicability in intelligent surveillance and emergency response management.