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<p>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.</p>

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Stampede detection and crowd analysis using CNN-LSTM and farneback optical flow

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Geetanjali Bhola,
  • Sumit Srivastava,
  • Hith Rahil Nidhan,
  • Sanyam Kathed

摘要

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.