Intelligent Traffic Surveillance for Real-Time Vehicle Detection, License Plate Recognition, and Speed Estimation

2026-01-01

arXiv.org

AI Summary

This study addresses the high incidence of traffic accidents caused by speeding in resource-constrained regions such as Uganda by proposing a lightweight, real-time intelligent traffic monitoring system. The system integrates YOLOv8 for license plate detection and a hybrid CNN-Transformer model for character recognition, achieving a remarkably low error rate of 1.79%. It further introduces an innovative region-of-interest-based method for vehicle speed estimation with an accuracy within ±10 km/h. By leveraging the Africa’s Talking communication API, the system enables automatic linkage to vehicle owner databases and sends real-time violation alerts via SMS, establishing an end-to-end automated enforcement pipeline. Experimental results demonstrate a license plate detection mAP of 97.9%, significantly outperforming existing solutions designed for low-resource settings.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Speeding is a major contributor to road fatalities, particularly in developing countries such as Uganda, where road safety infrastructure is limited. This study proposes a real-time intelligent traffic surveillance system tailored to such regions, using computer vision techniques to address vehicle detection, license plate recognition, and speed estimation. The study collected a rich dataset using a speed gun, a Canon Camera, and a mobile phone to train the models. License plate detection using YOLOv8 achieved a mean average precision (mAP) of 97.9%. For character recognition of the detected license plate, the CNN model got a character error rate (CER) of 3.85%, while the transformer model significantly reduced the CER to 1.79%. Speed estimation used source and target regions of interest, yielding a good performance of 10 km/h margin of error. Additionally, a database was established to correlate user information with vehicle detection data, enabling automated ticket issuance via SMS via Africa's Talking API. This system addresses critical traffic management needs in resource-constrained environments and shows potential to reduce road accidents through automated traffic enforcement in developing countries where such interventions are urgently needed.

Problem

• speeding
• traffic surveillance
• vehicle detection
• license plate recognition
• road safety

Innovation

• real-time traffic surveillance
• YOLOv8
• Transformer-based OCR
• speed estimation
• automated enforcement