Strategic traffic violation detection system

The Hybrid Traffic Safety System is an intelligent, AI-driven traffic monitoring and violation detection platform designed to improve road safety and automate traffic rule enforcement. The system integrates multiple computer vision–based detection modules Automatic Number Plate Recognition (ANPR), Helmet Detection, and Triple Ride Detection within a unified web-enabled architecture. Built using the MERN stack (MongoDB, Express.js, React.js, and Node.js), the platform supports real-time processing, scalable data management, and interactive visualization.

AI models developed using TensorFlow, PyTorch, and OpenCV analyze live and recorded surveillance footage to identify vehicles, recognize license plates, and detect rider safety violations with high accuracy. These machine learning components operate as independent microservices and communicate with the backend through secure RESTful APIs or WebSocket connections, enabling efficient separation of computation-intensive tasks from web services. Detected violations are stored along with timestamps, images, and metadata in a centralized database, allowing reliable evidence management and historical analysis.

The proposed hybrid architecture enhances system modularity, performance, and extensibility, making it suitable for large-scale urban deployment and smart city environments. By reducing dependence on manual monitoring and enabling continuous, real-time enforcement, the system provides a practical foundation for next-generation intelligent transportation systems aimed at improving traffic compliance and public safety.