Real-Time Intelligent Action-Based Surveillance System

References

[1] Y. Zhao, S. Tang, and M. Ye, "Adaptive Surveillance Video Compression with Background Hyperprior," IEEE Signal Process. Lett., vol. 32, pp. 456–460, 2025.

[2] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proc. IEEE CVPR, 2016, pp. 779–788.

[3] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv:2004.10934, 2020.

[4] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE CVPR, 2005, vol. 1, pp. 886–893.

[5] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. IEEE CVPR, 2001, vol. 1, pp. 1511–1518.

[6] N. Wojke, A. Bewley, and D. Paulus, "Simple online and realtime tracking with a deep association metric," in Proc. IEEE ICIP, 2017.

[7] J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv:1804.02767, 2018.

[8] Y. Liu, Z. Yuan, and J. Liu, "Future Frame Prediction for Anomaly Detection — A New Baseline," in Proc. IEEE CVPR, 2018, pp. 6536–6545.

[9] H. Kang, S. Park, and J. Kim, "Real-Time Fall Detection Using Pose Estimation and LSTM-Based Temporal Modelling," IEEE Access, vol. 11, pp. 12345–12356, 2023.

[10] B. Ramachandra, M. Jones, and R. Ranga Sai Vignesh, "A Survey of Single-Scene Video Anomaly Detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 5, pp. 2293–2312, 2020.

[11] W. Sultani, C. Chen, and M. Shah, "Real-world Anomaly Detection in Surveillance Videos," in Proc. IEEE CVPR, 2018, pp. 6479–6488.

[12] D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri, "A Closer Look at Spatiotemporal Convolutions for Action Recognition," in Proc. IEEE CVPR, 2018.

[13] T. Hassan, M. Mahmood, and A. Khan, "Automated CCTV Surveillance with Email Alert Generation for Perimeter Security," Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 3, 2021.

[14] G. Jocher et al., "Ultralytics YOLOv8," GitHub, 2023.

[15] F. Zhu, G. Sheng, W. Hu, and S. Gao, "Multi-Scale Spatial- Temporal Graph Convolutional Network for Skeleton-Based Action Recognition," in Proc. AAAI, 2021.

[16] Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei, and Y. Sheikh, "OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields," IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 1, pp. 172–186, 2021.

[17] J. Li, B. Li, and Y. Lu, "Deep Contextual Video Compression," in Proc. NeurIPS, 2021.

[18] M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis, "Learning Temporal Regularity in Video Sequences," in Proc. IEEE CVPR, 2016, pp. 733–742.

[19] S. Sabokrou, M. Fathy, M. Hoseini, and R. Klette, "Deep- Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes," Computer Vision and Image Understanding, vol. 172, pp. 88–97, 2018.

[20] R. Chalapathy and S. Chawla, "Deep Learning for Anomaly Detection: A Survey," arXiv:1901.03407, 2019.

[21] D. P. Kingma and M. Welling, "Auto-Encoding Variational Bayes," in Proc. International Conference on Learning Representations (ICLR), 2014.

[22] K. Simonyan and A. Zisserman, "Two-Stream Convolutional Networks for Action Recognition in Videos," in Proc. Advances in Neural Information Processing Systems, 2014.

[23] J. Carreira and A. Zisserman, "Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset," in Proc. IEEE CVPR, 2017.

[24] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proc. IEEE CVPR, 2016.

[25] W. Luo, W. Liu, and S. Gao, "A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework," in Proc. IEEE ICCV, 2017.

[26] Y. Cong, J. Yuan, and J. Liu, "Sparse Reconstruction Cost for Abnormal Event Detection," in Proc. IEEE CVPR, 2011.

[27] C. Lu, J. Shi, and J. Jia, "Abnormal Event Detection at 150 FPS in MATLAB," in Proc. IEEE ICCV, 2013.

[28] T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, "High Accuracy Optical Flow Estimation Based on a Theory for Warping," in Proc. European Conference on Computer Vision, 2004.

[29] A. Dosovitskiy et al., "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," in Proc. ICLR, 2021.

[30] C. Feichtenhofer, H. Fan, J. Malik, and K. He, "SlowFast Networks for Video Recognition," in Proc. IEEE ICCV, 2019.

[31] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.

[32] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training," in Proc. ICML, 2015.

[33] D. Xu, E. Ricci, Y. Yan, J. Song, and N. Sebe, "Learning Deep Representations of Appearance and Motion for Anomalous Event Detection," Pattern Recognition, vol. 89, pp. 390–401, 2019.

[34] J. Medel and A. Savakis, "Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks," arXiv:1612.00390, 2016.

[35] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, pp. 436–444, 2015.

@article{1717054,
      author = {Sai Kumar Yaradesi, Chittineni Suneetha, Tarun Sai Vallabhuni},
      title = {Real-Time Intelligent Action-Based Surveillance System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3702-3707},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717054.pdf},
      abstract = {Video surveillance systems play an important role in maintaining safety and security in public and private areas. However, traditional CCTV systems continuously record video without analysing activities in real time, which leads to large storage usage and delayed identification of abnormal events. To solve this problem, this paper presents a Real-Time Intelligent Action-Based Surveillance System using a custom trained YOLOv8 model to detect and classify human activities as normal or abnormal. The system highlights normal activities with green bounding boxes and abnormal activities with red bounding boxes, and automatically sends email alerts with captured images when suspicious behaviour is detected. In addition, the system records video only when activity is present, which helps in reducing unnecessary storage usage. The proposed system is implemented with a web interface for live monitoring and alert management. Experimental results show that the system achieves good detection accuracy while improving storage efficiency, making it suitable for real-time smart surveillance applications.},
      keywords = {YOLOv8, Human Activity Recognition, Anomaly Detection, Automated Alert System, Smart Surveillance, Event- Triggered Recording, Real-Time Detection, Abnormal Activity.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1717054}
  }