Design and Implementation of an IoT-Based Traffic Violation Detection Framework Using Image Processing

Rapid urbanisation, rising vehicle density and limited enforcement capacity have made traffic-rule compliance a major concern for public safety and urban administration. Conventional monitoring depends heavily on police personnel and manual inspection of closed-circuit television footage, which restricts coverage, delays response and increases the possibility of inconsistent decisions. This paper presents a compact design and implementation framework for an Internet of Things (IoT)-enabled traffic-violation detection system supported by image processing, computer vision, edge computing and secure event communication. The proposed architecture detects red-light jumping, absence of helmets, triple riding, wrong-way movement, lane misuse, illegal parking and over-speeding. Roadside cameras and sensors acquire traffic data, while an edge device performs frame enhancement, vehicle detection, tracking, behavioural analysis and automatic number-plate recognition. Once a probable violation is identified, the system generates a structured evidence package containing event images, a short video sequence, registration details, violation category, time, location and confidence score. Event-based communication reduces bandwidth consumption by transmitting only validated records rather than continuous video. A human verification stage is retained before administrative action in order to reduce false penalties and improve accountability. Since field observations and measured experimental results were not supplied, the paper defines a transparent validation plan based on precision, recall, F1-score, plate-recognition accuracy, latency, frame rate and bandwidth reduction. The framework offers a scalable foundation for smart-city traffic management while recognising the importance of privacy, cybersecurity, local calibration and legal oversight.