Real Time Vehicle Anti-Theft Detection and Tracking with IoT Notifications

This study focuses on developing a real time vehicle theft detection and leveraging IoT notifications for enhanced security. This system aims to ensure detection of unauthorized access using the Grassmann algorithm. The system consists of two groups for analysis: Group 1 includes a real-time anti-theft detection mechanism with 26 samples using the CNN algorithm, and Group 2 uses a Grassmann based approach with 26 samples. The study operates with a G Power value of 80%, a significance threshold of 0.05%, and a confidence interval of 95%. The system achieves faster processing time and instant notifications to the owner and accurate face recognition. The significance value is less than 0.05. In this work, it is observed Grassmann algorithm has better efficiency and makes an instant alert to the owner with the vehicle security technology where Group 2 (CNN) is better when it comes to accuracy, efficiency, and its real-world.