Citation :

Priyanka Parashar, Poonam Bhartiya, Shailendra Shriwastava, "Intelligent Crowd Monitoring and Early Warning Framework for Stampede Prevention Using YOLOv8m and CNN-LSTM Architecture," International Journal of Computer Trends and Technology (IJCTT), vol. 74, no. 6, pp. 12-21, 2026. Crossref, https://doi.org/10.14445/22312803/IJCTT-V74I6P102

Abstract

Railway stations, religious events, and other large public gatherings are prone to overcrowding, sometimes resulting in stampede situations. The existing systems for monitoring crowds primarily rely on manual monitoring, which prevents them from producing early warnings for potentially dangerous situations. In this paper, the authors describe a machine vision-enabled crowd monitoring and early warning framework that is based on YOLOv8m, CNN, and LSTM models with the ability to perform real-time analysis of crowds for the purpose of predicting the risk of a stampede. The framework uses real-time video streams from surveillance cameras for the processes of crowd detection, density estimation, motion analysis, flow direction tracking, and prediction of temporal behaviour. Results demonstrate that the proposed framework successfully identifies abnormal crowd behaviour and predicts the potential for extreme or dangerous situations prior to their occurring. The proposed system enhances overall crowd safety within a high-density area, enhances the accuracy of detecting abnormal behaviour, and improves the ability to provide early warning capability of a potentially dangerous crowd situation.

Keywords

Crowd Monitoring, YOLOv8m, CNN-LSTM, Preventing Stampedes, Computer Vision, Crowd Density Estimation, Early Warning System, Deep Learning.

References

[1] Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779-788, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Sepp Hochreiter, and Jürgen Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Dirk Helbing, and Péter Molnár, “Social Force Model for Pedestrian Dynamic,” Physical Review E, vol. 51, no. 5, pp. 4282-4286, 1995.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, 25, pp. 1097-1105, 2012.
[Google Scholar] [Publisher Link]

[5] Zhong-Qiu Zhao et al., “Object Detection with Deep Learning: A Review,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212-3232, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Rabia Nasir et al., “An Enhanced Framework for Real-Time Dense Crowd Abnormal Behavior Detection using YOLOv8,” Artificial Intelligence Review, vol. 58, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Yingying Zhang et al., “Single-Image Crowd Counting via Multi-Column Convolutional Neural Network,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 589-597, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Lu Zhang, Miaojing Shi, and Qiaobo Chen, “Crowd Counting via Scale-Adaptive Convolutional Neural Network,” 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, pp. 1113-1121, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Tarik Alafif et al., “Toward an Integrated Intelligent Framework for Crowd Control and Management (IICCM),” IEEE Access, vol. 13, pp. 58559-58575, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Tarik Alafif et al., “Generative Adversarial Network based Abnormal Behavior Detection in Massive Crowd Videos: A Hajj Case Study,”  Journal of Ambient Intelligence and Humanized Computing, vol. 13, pp. 4077-4088, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, 2012.
[Google Scholar] [Publisher Link]

[12] Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016.
[Google Scholar] [Publisher Link]

[13] Jeffrey Donahue et al., “Long-Term Recurrent Convolutional Networks for Visual Recognition and Description,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2625-2634, 2015.
[Google Scholar] [Publisher Link]

[14] Utkarsh Singh et al.,”Crowd Monitoring: State-of-the-Art and Future Directions,” IETE Technical Review, vol. 38, no. 6, pp. 578-594, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Russell J. Stuart, and Peter Norvig, Artificial intelligence: A Modern Approach, 3^rd ed., Pearson, 2015.
[Google Scholar] [Publisher Link]

[16] Richard G. Baraniuk et al., “Compressive Video Sensing: Algorithms, Architectures, and Applications,” IEEE Signal Processing Magazine, vol. 34, no. 1, pp. 52-66, 2017. 
[CrossRef] [Google Scholar] [Publisher Link]

[17] Jonathan Long, Evan Shelhamer, and Trevor Darrell, “Fully Convolutional Networks for Semantic Segmentation,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 3431-3440, 2015.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Shaoqing Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Poonam Bhartiya, Mukta Bhatele, and Akhilesh A Waoo, Detection and Identification of Objects to Enhance Traffic Sign Recognition using Deep Convolutional Neural Networks, 38^th Indian Engineering Congress, Reimagining Tomorrow: Shaping the Future through Disruptive and Interdisciplinary Technologies, Jabalpur, pp. 145-153, 2023. [Online]. Available: https://www.researchgate.net/publication/393782649_Detection_and_Identification_of_Objects_to_Enhance_Traffic_Sign_Recognition_using_Deep_Convolutional_Neural_Networks

[20] Poonam Bhartiya, Mukta Bhatele, and Akhilesh A Waoo, “A Machine Learning Approach for Predictive Analysis of Traffic Flow,” ShodhKosh: Journal of Visual and Performing Arts, vol. 5, no. 5, pp. 422-430, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[21]  Junzhou Chen et al., “A Three-Stage Anomaly Detection Framework for Traffic Videos,” Journal of Advanced Transportation, vol. 2022, 2022.
[CrossRef] [Google Scholar] [Publisher Link] 

[22] Waqas Sultani, Chen Chen, and Mubarak Shah, “Real-World Anomaly Detection in Surveillance Videos,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6479-6488, 2018.
[Google Scholar] [Publisher Link]