DEEPVISION GUARD: AN INTELLIGENT DEEP LEARNING BASED CCTV SURVEILLANCE SYSTEM FOR REAL-TIME THREAT DETECTION AND ALARM GENERATION

Author

Vinotha J, Mrs. P. Jasmine Lois Ebenazar

Abstract

 The increasing use of Closed-Circuit Television (CCTV) cameras in public places, industries, educational institutions, commercial buildings, and residential areas has significantly improved security monitoring. However, continuously observing multiple CCTV streams is a challenging task, as human operators may become fatigued, distracted, or fail to notice critical events in time. This limitation can result in delayed responses to emergencies such as fire outbreaks, unauthorized intrusions, or the presence of dangerous weapons. Therefore, there is a growing need for an automated surveillance system capable of detecting potential threats accurately and responding immediately without relying solely on human intervention. The motivation behind this project is to develop an intelligent surveillance solution that enhances public safety through real-time video analysis. Recent advances in Deep Learning and Computer Vision have made it possible to identify objects and events with high accuracy directly from live video feeds. By integrating these technologies into existing CCTV infrastructure, security systems can become more proactive, reliable, and efficient while minimizing false alarms and reducing the workload of security personnel. This project proposes DeepVision Guard, a deep learning-based object monitoring system that continuously analyzes CCTV footage to identify predefined threat categories such as fire, humans, and weapons. The system employs a state-of-the-art object detection model to detect and classify objects in every video frame. When a threat is identified with a confidence score exceeding a predefined threshold, the system immediately activates an alarm and records the detected event with its timestamp. The captured evidence can also be stored for future investigation and security auditing. The proposed system is designed to process live video streams in real time while maintaining high detection accuracy under different lighting conditions and environmental variations. The primary objectives of this project are to develop an automated real-time surveillance system using YOLO deep learning algorithm, accurately detect critical objects such as fire, persons, and weapons from CCTV footage, generate instant alerts to enable rapid emergency response, reduce dependence on continuous manual monitoring, and improve the overall effectiveness of modern security systems. The proposed solution can be deployed in schools, colleges, hospitals, shopping malls, airports, railway stations, warehouses, factories, office buildings, and smart city surveillance networks, providing a scalable and intelligent approach to public safety and threat prevention.

Keywords

DeepVision Guard, CCTV Surveillance, YOLO, Deep Learning, Object Detection, Computer Vision, Real-Time Threat Detection, Fire and Weapon Detection

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References

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