AN AI-POWERED SURVEILLANCE SYSTEM FOR WEAPON AND VIOLENCE DETECTION IN REAL-TIME CCTV STREAMS | IJCT Volume 13 – Issue 3 | IJCT-V13I3P36
International Journal of Computer Techniques
ISSN 2394-2231
Author
SRIDEVI P, SHAFA D, SAFA SULAIHA A, CIME MONIKA
Abstract
Public safety in crowded environments such as transportation hubs, public gatherings, and critical infrastructures is a significant concern. Traditional surveillance systems rely heavily on manual monitoring, which is prone to human fatigue, delayed response, and missed incidents. This paper proposes an AI-powered surveillance framework that utilizes deep learning techniques for real-time detection of weapons and violent activities in CCTV video streams. The system integrates YOLO (You Only Look Once) for object detection and CNN-LSTM models for action recognition. The proposed framework enables automated monitoring, instant threat detection, and real-time alert generation. Experimental results demonstrate improved efficiency, faster response time, and enhanced security compared to conventional surveillance systems.
Keywords
Artificial Intelligence, Surveillance System, YOLO, CNN-LSTM, Weapon Detection, Violence Detection, Computer Vision
Conclusion
The proposed AI-powered surveillance system effectively detects weapons and violent activities in real-time CCTV streams using YOLO and CNN-LSTM models. By automating threat detection, the system reduces dependence on manual monitoring and significantly improves response time and overall security efficiency. The integration of real-time alerts, evidence capture, and data storage ensures a reliable and proactive surveillance solution.
References
1.J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified Real-Time Object Detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.
2.J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 7263–7271, doi: 10.1109/CVPR.2017.690.
3.A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
4.C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2023.
5.G. Jocher et al., “Ultralytics YOLOv8: State-of-the-Art Real-Time Object Detection,” GitHub repository, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics
6.M. Sultani, C. Chen, and M. Shah, “Real-World Anomaly Detection in Surveillance Videos,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018, pp. 6479–6488, doi: 10.1109/CVPR.2018.00678.
7.B. Hassner, Y. Itcher, and O. Kliper-Gross, “Violent Flows: Real-Time Detection of Violent Crowd Behavior,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2012, pp. 1–6, doi: 10.1109/CVPRW.2012.6239348.
8.A. Sajjad, A. Khan, K. Muhammad, S. Rho, and S. W. Baik, “Multi-Grade Deep Learning for Violence Detection in Smart Surveillance Systems,” IEEE Access, vol. 7, pp. 124–138, 2019, doi: 10.1109/ACCESS.2018.2885039.
9.R. Verma, A. Singh, and N. Sharma, “Real-Time Weapon Detection Using Deep Learning for Surveillance Applications,” in Proc. Int. Conf. Computing and Communication Systems (ICCCS), 2022, pp. 1–6, doi: 10.1109/ICCCS55188.2022.10079776.
S. K. Yadav, A. Sharma, and P. Gupta, “YOLOv8-Based Real-Time Weapon Detection and Classification System,” in Proc. Int. Conf. Smart Computing and Artificial Intelligence (ICSCAI), 2024, pp. 1–6.
How to Cite This Paper
SRIDEVI P, SHAFA D, SAFA SULAIHA A, CIME MONIKA (2026). AN AI-POWERED SURVEILLANCE SYSTEM FOR WEAPON AND VIOLENCE DETECTION IN REAL-TIME CCTV STREAMS. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
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