Deep Learning Based Vehicle Speed Estimation on Highways

Abstract

Traffic management always is a matter requiring the attention of highway system managers in terms of vehicle monitoring and speed estimation. This paper proposes an efficient deep learning-based vehicle speed estimation on highway lanes in the Vietnam transport system. The input videos are recorded by fixed surveillance cameras. An optimized single shot multibox detector network, called SSD, is utilized for vehicle license plate detection (LPD). The deep SORT (simple online and real-time tracking) model is first applied to video vehicle tracking and performed in the detected license plate area. This tracking process investigates the traveling distance of a vehicle to estimate its speed. In this study, the dataset has been normalized to improve the efficiency of vehicle localization and tracking to improve the time elapsing in the estimation of the distance traveled by vehicles on highways. The results showed that the proposed system has achieved better accuracy in terms of the determined speeds with errors ranging between [-1.5, +1.1] km/h, equivalent to 98% of the error limit by the regulation in Viet Nam.