Modern urban centers deploy vast networks of CCTV and Automatic Number Plate Recognition(ANPR) cameras to manage traffic, enforce traffic laws, and maintain public security. However,most existing systems process these feeds in isolated silos, performing basic license plate detection without effectively linking data across space and time. This lack of integration prevents city authorities from automatically tracking high-interest vehicles across different sectors and limits their ability to extra
The objective is to develop a robust, centralized AI software system that processes multicamera feeds across a city-wide ANPR network to accomplish three core functionalities.
Modern urban centers deploy vast networks of CCTV and Automatic Number Plate Recognition(ANPR) cameras to manage traffic, enforce traffic laws, and maintain public security However,most existing systems process these feeds in isolated silos, performing basic license plate detection without effectively linking data across space and time This lack of integration prevents city authorities from automatically tracking high-interest vehicles across different sectors and limits their ability to extra
The objective is to develop a robust, centralized AI software system that processes multicamera feeds across a city-wide ANPR network to accomplish three core functionalities. First, the system must feature a High-Accuracy ANPR and OCR Engine, which utilizes an advanced Optical Character Recognition model that can achieving greater than 90% accuracy across diverse realworld conditions such as varying lighting, poor weather, angled shots, motion blur, and dirty or damaged license plates. Second, it requires a Single Plate Trajectory Tracking module to build a spatial-temporal tracking system that can reconstructing the complete travel trajectory of any specific vehicle plate across the entire city network. This system will map a vehicle's movement history, timestamps, direction, and