An Intelligent Next-Generation surveillance and Safety System using Deep Learning

Public safety in urban environments demands intelligent and adaptive surveillance solutions to mitigate risks effectively. Traditional CCTV-based monitoring systems suffer from limitations such as delayed threat detection, human error, and inefficiency in high-density areas. To address these challenges, this research introduces The Smart Secure Next-Gen Surveillance System, an AI-powered solution integrating YOLOv8 and real-time analytics for enhanced security. The system employs an optimized YOLOv8 model trained on the COCO dataset to detect anomalies in crowd behavior, enabling early identification of potential security threats. Additionally, a real-time fire detection module ensures rapid emergency response by analyzing live video feeds for fire hazards. To further bolster security, the system implements restricted area monitoring, preventing unauthorized access through AI-driven alerts. By leveraging deep learning algorithms, computer vision, and real-time processing, the proposed system significantly enhances situational awareness and response efficiency in public spaces. The research highlights the system’s ability to improve threat detection accuracy, reduce manual intervention, and minimize false alarms. Extensive testing in simulated urban scenarios demonstrates its effectiveness in handling real-world security challenges. The results suggest that AI-driven surveillance, powered by YOLOv8, can revolutionize public safety by transitioning from passive monitoring to proactive, intelligent threat detection and response mechanisms.