Harnessing Semantic and Trajectory Analysis for Real-Time Pedestrian Panic Detection in Crowded Micro-Road Networks

Pedestrian panic behavior is a primary cause of overcrowding and stampede accidents in public micro-road network areas with high-density pedestrians. However, reliably detecting such behaviors remains challenging due to their inherent complexity, variability, and stochastic nature. Current detection models often rely on single-modality features, limiting their effectiveness in complex, dynamic crowd scenarios. To overcome these limitations, this study proposes a novel multimodal panic detection approach integrating crowd density mapping, pedestrian trajectory analysis, and semantic recognition. Specifically, crowd density maps are generated using a convolutional neural network (CDNet) to identify regions with abnormal density gradients via contour analysis. Within these potential panic zones, pedestrian trajectories are analyzed through LSTM networks to capture irregular movements such as counterflow and nonlinear wandering behaviors. Concurrently, semantic recognition based on Transformer models is utilized to identify verbal distress cues extracted through Baidu AI real-time speech-to-text conversion. These multi-modal features—spatial, temporal, and semantic—are systematically fused and weighted using an MLP-based feature fusion framework to achieve robust panic detection accuracy. Comprehensive experiments on the UCF Crowd dataset demonstrate that this proposed approach significantly outperforms state-of-the-art methods, achieving an accuracy of 91.7%. The proposed detection framework can technically support real-time crowd safety management further in high-density pedestrian scenarios, including significant public crowd-gathering activities, transportation hubs, and emergency evacuations.