Theft Detection using Image Processing

Abstract

Theft detection by image processing is a critical security measure for protecting expensive assets. Manual surveillance is a major component of traditional security systems, which often causes detection and reaction delays. Security cameras are a common component of current systems, but they don't always detect threats in real time or send out automatic notifications. Our solution is a state-of-the-art live camera-based theft detection system that makes use of the VGG16 deep learning model to identify objects and detect anomalies. If the system detects any suspicious activity in the live video feeds, it will quickly warn the user via an alarm system that includes an audio alert. Benefits of this method include less need for human interaction, excellent accuracy, and real-time monitoring. Improving safety via faster and more accurate theft detection and notification is the main objective. The fundamental issue that has been recognized is that conventional systems are unable to provide immediate and automatic answers. Our solution utilizes deep learning and image processing to provide a fullscale theft detection system with real-time monitoring and notifications by voice, guaranteeing prompt preventative measures.