SMART SECURITY SURVEILLANCE SYSTEM USING IoT FOR CRIME DETECTION

Abstract

With the growing need for enhanced security, traditional surveillance systems that rely on manual monitoring are no longer sufficient. Real-time, automated surveillance has become a critical area of computer vision, playing an increasingly vital role in public safety. Surveillance cameras, when paired with visible warning signs, can deter criminal activity by recording footage that assists in identifying and tracking individuals. Building on this foundation, integrating Wi-Fi-enabled IoT devices can significantly enhance system capabilities.This project proposes a smart security surveillance system that leverages Convolutional Neural Networks (CNN) for crime detection and recognition tasks. Utilizing the ESP32-CAM module — an efficient IoT solution offering Wi-Fi connectivity and low-power communication — the system enables real-time analysis of surveillance footage. When a known individual is detected within the monitored area, the system promptly sends a notification. If an unfamiliar person or suspicious activity is detected through motion sensors and CNN-based analysis, the system issues an alert along with a captured video clip.