AI-Based Home Security Surveillance System

The rising security threats in smart home environments highlight the need for intelligent, automated protection systems, and this project introduces an AI-driven Home Security Surveillance System that integrates IoT sensors with machine learning models to deliver real-time monitoring and threat detection. Using technologies such as Python, OpenCV, Arduino, and GSM modules, the system incorporates facial recognition, gas and fire sensors, LDRs, and surveillance cameras to detect intrusions and immediately alert homeowners. Algorithms like Haar Cascade, LightGBM, and XGBoost enable accurate face detection and efficient intrusion classification, while features such as live video streaming, face database matching, SMS notifications, and remote control enhance usability and responsiveness. Built on a layered architecture that supports authentication, data logging, and intelligent decision-making, the platform offers a scalable, low-cost, and energy-efficient security solution that advances traditional home surveillance through automation, anomaly detection, and user-specific insights.