AI Enhanced Smart Surveillance

Abstract— To address the growing limitations of traditional surveillance systems, such as delayed response times, human error, and a lack of proactive monitoring, this project introduces an AI-driven smart surveillance system designed to detect and mitigate illegal activities such as theft, violations, accidents, and fighting in real time. Using advanced deep learning techniques such as YOLOv11 for precise object detection and autoencoders for anomaly detection, the system accurately identifies suspicious behaviors and sends instant alerts, including phone calls, to authenticated personnel, along with the incident location. The system's capacity to automate threat detection and response considerably decreases the need for manual monitoring, shortens response times, and improves overall security efficiency. Integrating real-time video analysis and cloud-based data storage. The system ensures scalability and seamless deployment across a variety of scenarios. Key findings illustrate the system's high precision in detecting and classifying numerous security concerns, allowing for prompt intervention via timely notifications. This study tackles important holes in modern surveillance by providing a scalable, flexible, and efficient approach that improves situational awareness and threat prevention. This initiative, which combines cutting-edge AI technology with real-time alert systems, lays the way for safer, more secure surroundings in both public and private places, representing a significant leap in the field of smart surveillance. KEYWORDS: Smart Surveillance, Artificial Intelligence (AI), Real-Time Threat Detection, YOLOv11, Anomaly Detection, Facial Recognition, Behavior Analysis, Cloud-Based Surveillance, Autonomous Monitoring, Theft Detection, Tamper Detection, Real-Time Alerts, Deep Learning, Machine Learning (ML), Video Analytics, Security Automation, Proactive Surveillance, Unlawful Activity Detection, Situational Awareness