Multi-Modal AI Architecture for Real-Time Detection and Tracking of Stolen Vehicles

Vehicle theft remains a significant global issue, where current solutions often fall short due to their overreliance on GPS trackers, manual monitoring, and disconnected law enforcement systems. This paper presents a comprehensive AI- controlled monitoring structure that revolutionizes detection and tracking of vehicles. The proposed system integrates computer vision in real time using CCTV and drone recording, future indicative analysis for route assessment and blockchain- based verification of vehicles. Unlike traditional methods, our solution identifies stolen vehicles, even with converted license plates or missing GPS devices, leverages visual features such as models, color and damage patterns. In addition, a law enforcement dashboard ensures immediate notice and spontaneous coordination. Experimental assessment shows high identification accuracy, reduces false positivity and increases the reaction rate, making it a viable candidate for smart city infrastructure. This proposed surveillance system has significant potential to prevent crime, urban traffic management and insurance confirmation, for safe and more responsible urban environment.