Computer Vision Applications in Intelligent Transportation Systems: A Survey

Abstract

As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

A Survey on Vehicular Traffic Flow Anomaly Detection Using Machine Learning

“…The main contributions of this paper are to reveal the data source, data processing, algorithm selection, and model evaluations used by recent research and to provide emerging research opportunities from a different point of view that focuses on the technical aspect of ML applications. These two contributions make this paper different from the existing surveys and reviews [15,16].…”

“…However, it was found that limited literature mainly focuses on detecting traffic flow anomalies. To the best of our search of existing surveys and reviews about traffic flow anomaly detection using ML, two published survey and review papers partially like our paper [15,16]. The limitations of these papers have been identified and can be explained as follows.…”

A Road Behavior Pattern-Detection Model in Querétaro City Streets by the Use of Shape Descriptors

“…In the current era, computer vision research lines mention several approaches for vehicle classification and detection [15]. Thirumarai and Amudha [13] proposed a novel video surveillance technique with an image segmentation algorithm to track moving objects in a crosswalk of a road.…”

Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles

“…LDWS, a key component in advancing driver assistance technologies, actively contributes to road safety by alerting drivers of unintentional lane deviations [ 6 , 7 , 8 , 9 , 10 ]. The performance of LDWS is dependent on the clarity and detectability of road markings, which can be influenced by factors such as weather, wear, and lighting conditions [ 11 , 12 , 13 ]. That is, this system’s functionality is intricately linked to environmental factors, particularly road marking visibility and conditions.…”