Smart Security System Based On Intrusion And Anomaly Detection

Abstract

The prevalence of security threats necessitates innovative solutions for effective surveillance and threat detection. In response, we introduce our final year project, the ’Smart Security System Based on Intrusion and Anomaly Detection.’ This project addresses the need for real-time monitoring and identification of security risks by seamlessly integrating facial detection and weapon detection technologies. Leveraging machine learning models, our system provides continuous surveillance through live camera streams, detecting recognized faces and weapons with high accuracy. Upon detection, alerts are promptly generated and stored within the designated room, accompanied by essential details such as timestamps, person names, and room numbers. Users have the flexibility to switch camera feeds effortlessly, enabling comprehensive monitoring across multiple rooms. The system ensures security through the tracking of individuals’ movements, and identifying potential threats based on historical data. Notably, our system prioritizes data security, storing all information securely in a database and safeguarding user credentials through a robust registration and login system. Furthermore, it incorporates advanced algorithms for real-time video processing, facilitating swift response to security incidents and enhancing overall situational awareness. By harnessing the power of machine learning and real-time surveillance, it empowers users with the tools needed to mitigate security risks effectively. We believe that our project holds significant potential for enhancing security measures and safeguarding individuals and assets in various settings.