Deep Learning Based Framework For Chary Presence Monitoring Through Intelligent Surveillance
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Abstract
The increasing demand for intelligent surveillance systems has motivated the development of automated methods capable of detecting and classifying abnormal human behaviors in real time. This paper presents a deep learning- driven framework for the recognition of Chary presence activities in surveillance video streams. Leveraging convolutional and temporal modeling techniques, the proposed system extracts robust spatial-temporal features to accurately classify both normal and abnormal behaviors such as loitering, fighting, theft, and vandalism. Publicly available benchmark datasets, including UCF-Crime and Avenue, were used to evaluate the system, providing a diverse range of real-world scenarios for comprehensive performance assessment. Experimental results demonstrate that the proposed framework outperforms conventional baselines such as CNN+LSTM, 3D- CNN, and handcrafted feature-based methods in terms of accuracy, precision, recall, and F1-score. Furthermore, the model achieves near real-time performance with an inference speed of approximately 30 fps, highlighting its suitability for practical deployment in large-scale monitoring systems. These findings confirm the effectiveness and scalability of the framework as a reliable solution for enhancing public safety through intelligent surveillance.
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References
Simonyan, K., & Zisserman, A. (2014). Two-Stream Convolutional Networks for Action Recognition in Videos. Advances in Neural Information Processing Systems (NeurIPS), 27, 568–576.
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning Spatiotemporal Features with 3D Convolutional Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 4489–4497.
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2015). Long-term Recurrent Convolutional Networks for Visual Recognition and Description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2625–2634.
Carreira, J., & Zisserman, A. (2017). Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6299–6308.
Harishchandra Patel, “Impedance Control in HDI and Substrate-Like PCBs for AI Hardware Applications” (2024). Journal of Electrical Systems, 20(11s), 5109-5115.
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., & Davis, L. S. (2016). Learning Temporal Regularity in Video Sequences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 733–742.
Sultani, W., Chen, C., & Shah, M. (2018). Real-World Anomaly Detection in Surveillance Videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6479–6488.
Bertasius, G., Wang, H., & Torresani, L. (2021). Is Space-Time Attention All You Need for Video Understanding? Proceedings of the 38th International Conference on Machine Learning (ICML), 139, 813–824.
Deshpande, K., Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2022). Anomaly Detection in Surveillance Videos Using Transformer Based Attention Model. International Journal of Information Management Data Insights, 2(2), 100103.
Duong, D., Le, H., & Nguyen, T. (2023). A Comprehensive Survey on Video Anomaly Detection: Challenges, Methods, and Future Directions. ACM Computing Surveys, 55(12), 1– 36.
Anoopa, V. (2022). Video Anomaly Detection: A Review of Recent Trends. Journal of Visual Communication and Image Representation, 83, 103459.
Mahareek, A. (2024). Deep Learning-Based Approaches for Anomaly Detection in Surveillance Videos: A Survey. International Journal of Trends in Artificial Intelligence Research (IJTAR), 7(1), 45–61.
Sultani, W., Chen, C., & Shah, M. (2018). Real-world anomaly detection in surveillance videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6479–6488.
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, 3–11.
Carreira, J., & Zisserman, A. (2017). Quo Vadis, action recognition? A new model and the Kinetics dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4724–4733.
Singh, S., & Neelam, P. (2020). Real-time human activity recognition using deep learning for smart surveillance systems. Procedia Computer Science, 167, 2241–2248.
Luo, W., Liu, W., & Gao, S. (2017). A revisit of sparse coding-based anomaly detection in stacked RNN framework. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 341–349.
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50× fewer parameters. arXiv preprint arXiv:1602.07360.
Deniz, O., Serrano, I., Bueno, G., & Kim, T.-K. (2016). Fast violence detection in surveillance videos using ConvNet and motion features. Computer Vision and Image Understanding, 144, 177–186.
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning spatiotemporal features with 3D convolutional networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 4489–4497.