A Novel Algorithm for Estimating Fast-Moving Vehicle Speed in Intelligent Transport Systems

Intelligent Transport System (ITS) has been considered is the ultimate goal of traffic management in the 21st century. ITS is hoped to create a more efficient transport system and safer traffic experience. An ITS comprises many components of which traffic data collection is one of the essential functionalities. This data collection component is responsible for collecting various kinds of data on which the system relies to make responses to traffic conditions. One of the most important data to be collected is vehicle speed. With the rapid development of artificial intelligence, computer vision based techniques have been used increasingly for vehicle speed estimation. However, most techniques focus on daytime environment. This paper proposes a novel algorithm for vehicle speed estimation. Transfer learning with YOLO is used as the backbone algorithm for detecting the vehicle taillights. Based on the distance between two taillights, a model that combines camera geometry and Kalman filters is proposed to estimate the vehicle speed. The advantage of the proposed algorithm is that it can quickly estimate the vehicle speed without prerequisite information about the vehicle which to be known as in many existing algorithms. Furthermore, the processing time of the proposed algorithm is very fast thanks to the backbone deep learning model. Owing to the Kalman filter, the proposed algorithm can achieve very high level of speed estimation accuracy. In this paper, the performance of the proposed algorithm is verified through experiment results.