Finding Stolen Vehicles Using Traffic Cameras

Abstract: A modern tracking system based on traffic cameras will be detailed in this document to improve vehicle theft recovery rates particularly during plate obscuring or removal situations. The standard stolen vehicle detection system experiencing difficulties with license plate recognition relies primarily on Automatic License Plate Recognition (ALPR). The proposed system connects ALPR technology with contemporary machine learning and computer vision features to enable detections of vehicles by model-specific attributes like vehicle shape and color type. lượt động cơ yếu tố dissipation from a wide network of traffic cameras enables the system to provide instant location updates to law enforcement agencies. The system generates instantaneous alerts about located stolen vehicles which contain specific information regarding position data and travel direction and timestamp. Swift emergency responses become possible through this technology which increases the odds of stolen vehicle recovery and criminal capture. The system implements two machine learning models (Convolutional Neural Networks and YOLO) which detect and categorize vehicles under any lighting circumstances or when obscured by other objects. The connection of current traffic cameras to these technologies permits efficient and scalable tracking services in real-time. The paper shows how machine learning tools with computer vision technology integrated with traffic cameras create an efficient solution to detect stolen vehicles which delivers practical data to police services that boosts public protection rates.