Stampede incidents in densely populated environments remain a critical challenge for public safety, resulting in severe casualties and significant disruption. This paper presents a novel, data-driven framework for automated stampede detection and crowd risk classification, leveraging the integration of Farneback optical-flow computation with a hybrid CNN-LSTM architecture. Two complementary datasets, UCSD Anomaly detection dataset and Agoraset dataset, were combined to capture a broad spectrum of crowd behaviors and densities. The proposed system classifies crowd states into four distinct risk levels: normal, moderate, dense, and risky, thereby offering a more granular assessment than traditional binary models. The model was trained and evaluated on a balanced dataset of 10,000 annotated frames, with rigorous preprocessing and augmentation to ensure robustness. Experimental results demonstrate an accuracy of 99.75%, further cross-dataset evaluation on the UMN benchmark assesses the robustness under domain shift conditions. While the approach shows strong potential for real-time deployment in public event management and emergency response, current limitations include computational latency and challenges in ultra-dense, occluded scenarios.
Bhola et al. (Fri,) studied this question.