ReviewSensors (Basel, Switzerland)2023
Computer Vision Applications in Intelligent Transportation Systems: A Survey.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
15 citing papers in PubMed.
- Bus-Mounted Vision Sensing for Traffic Object Detection: BFTD and a Local-Global Attention Framework.Sensors (Basel, Switzerland) · 2026Article
- Forecasting Congestion and Enabling Proactive Management on Expressways During Public Holidays: A Survey of Methods, Open Issues, and Research Directions.Sensors (Basel, Switzerland) · 2026Review
- STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments.Sensors (Basel, Switzerland) · 2026Article
- DW-ReID: Vision-Language Learning for Person Re-Identification Under Diverse Weather Conditions.Sensors (Basel, Switzerland) · 2026Article
- Mapping the adoption of artificial intelligence in urban mobility.Open research Europe · 2026Article
- Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning.Scientific reports · 2025Article
- Deep BiLSTM Attention Model for Spatial and Temporal Anomaly Detection in Video Surveillance.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Assessment Techniques for Abnormal Movement Patterns in Neurological Disorders: Applications and Advances.Behavioural neurology · 2025Review
- Enhanced lightweight and compromised-resilient image encryption for resource constrained environments.PloS one · 2025Article
- Article
- Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based Classification with PyQt5 Implementation.Sensors (Basel, Switzerland) · 2024Article
- A Review of Application of Deep Learning in Endoscopic Image Processing.Journal of imaging · 2024Review
- Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles.Sensors (Basel, Switzerland) · 2024Article
- A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning.Biomimetics (Basel, Switzerland) · 2024Article
- A lightweight grape detection model in natural environments based on an enhanced YOLOv8 framework.Frontiers in plant science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.