Zhao  
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; [EMAIL_ADDRESS] (L.H. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (B.W. ):  
[EMAIL_ADDRESS] (A.R. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (Y.M.) Zhao  
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; [EMAIL_ADDRESS] (L.H. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (B.W. ):  
[EMAIL_ADDRESS] (A.R. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (Y.M.) Zhao  
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; [EMAIL_ADDRESS] (L.H. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (B.W. ):  
[EMAIL_ADDRESS] (A.R. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (Y.M.) Zhao  
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; [EMAIL_ADDRESS] (L.H. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (B.W. ):  
[EMAIL_ADDRESS] (A.R. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (Y.M.) Zhao  
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; [EMAIL_ADDRESS] (L.H. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (B.W. ):  
[EMAIL_ADDRESS] (A.R. ): [EMAIL_ADDRESS] (C.L. ): [EMAIL_ADDRESS] (Y.M.) Zhao  
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; [EMAIL_ADDRESS] (L.H. ): [EMAIL_ADDRESS] (C.L.H. ): [EMAIL_ADDRESS] (B.W. ):  
arifurahmantopu883 behaviors because they are complex, variable, and stochastic [2]. Recent advancements in computer vision and sensor technologies have facilitated the development of crowd monitoring systems [4; 5]. Traditional approaches, such as surveillance cameras and sensor-based tracking, provide valuable insights into crowd behavior. Nevertheless, these methods often face limitations in real-time panic detection due to their reliance on sparse data, isolated analysis of crowd density, or lack of individual behavioral interpretation [6]. Detecting panic behaviors, such as sudden changes in movement patterns or the formation of dangerous density gradients, requires more sophisticated, multi-dimensional approaches that can capture individual and collective responses to perceived threats [7]. Several studies have investigated the use of density maps to assess crowd congestion and predict potential hazards [8; 9]. However, existing methods typically focus on spatial density without considering dynamic factors, such as individual trajectories or semantic cues that may indicate distress. While trajectory analysis has been employed to study pedestrian movement [10], it is often disconnected from the contextual interpretation of behavior. While traditional trajectory modeling often relies on predefined physical assumptions, recent work has explored model-free learning of dynamic systems through multi-layer neural networks [11], suggesting alternative ways to capture complex behaviors without explicit physical modeling. Some recent approaches have attempted to integrate social and psychological factors, such as speech analysis and sentiment detection [12; 13]; however, these remain underexplored in crowd panic. While prior work has explored multimodal fusion, many methods rely on simple concatenation without modeling inter-modal relationships. In contrast, recent AloT-based activity recognition research has demonstrated that structured fusion methods--such as attention-based, hierarchical, or graph neural networks--can better capture complementary features across modalities. As reviewed in [14], these advanced approaches improve behavior recognition but often come with high computational costs, limiting their suitability for real-time public safety applications. To address this issue, this study proposes a data-driven framework that integrates multiple sources of information--density maps, contour-based analysis, trajectory recognition, and semantic interpretation--to identify and localize panic behaviors in moving crowds.