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

TL;DR

This paper presents a real-time traffic surveillance system using computer vision for vehicle detection, license plate recognition, and speed estimation, tailored for resource-limited regions like Uganda.

Contribution

It introduces a comprehensive traffic monitoring system combining YOLOv8, CNN, and transformer models for license plate recognition and integrates automated ticketing, addressing safety in developing countries.

Findings

License plate detection with YOLOv8 achieved 97.9% mAP.

Transformer model reduced character error rate to 1.79%.

Speed estimation had a 10 km/h margin of error.

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.