ArticleSensors (Basel, Switzerland)2018
Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Wafer-level meta-aspheric lenses for compact wide-FOV NIR imaging.Light, science & applications · 2026Article
- In-vehicle 3D vision for perceiving dangerous driving behaviors.Scientific reports · 2026Article
- Gaze Zone Classification for Driving Studies Using YOLOv8 Image Classification.Sensors (Basel, Switzerland) · 2024Article
- Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.Sensors (Basel, Switzerland) · 2024Review
- Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.PloS one · 2024Article
- Article
- Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Road Feature Detection for Advance Driver Assistance System Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- 3D Gaze Estimation Using RGB-IR Cameras.Sensors (Basel, Switzerland) · 2022Article
- Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator.Sensors (Basel, Switzerland) · 2022Article
- Review and Evaluation of Eye Movement Event Detection Algorithms.Sensors (Basel, Switzerland) · 2022Review
- Single Camera Face Position-Invariant Driver's Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Gaze Estimation Approach Using Deep Differential Residual Network.Sensors (Basel, Switzerland) · 2022Article
- The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process.Sensors (Basel, Switzerland) · 2021Article
- Deep learning-based facial image analysis in medical research: a systematic review protocol.BMJ open · 2021Article
- Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental Scenarios.Sensors (Basel, Switzerland) · 2021Article
- Survey and Synthesis of State of the Art in Driver Monitoring.Sensors (Basel, Switzerland) · 2021Review
- Does agreement mean accuracy? Evaluating glance annotation in naturalistic driving data.Behavior research methods · 2021Article
- Convolutional Neural Network-Based Technique for Gaze Estimation on Mobile Devices.Frontiers in artificial intelligence · 2021Article
- Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A paradigm shift is required to prevent the increasing automobile accident deaths that are mostly due to the inattentive behavior of drivers. Knowledge of gaze region can provide valuable information regarding a driver's point of attention. Accurate and inexpensive gaze classification systems in cars can improve safe driving. However, monitoring real-time driving behaviors and conditions presents some challenges: dizziness due to long drives, extreme lighting variations, glasses reflections, and occlusions. Past studies on gaze detection in cars have been chiefly based on head movements. The margin of error in gaze detection increases when drivers gaze at objects by moving their eyes without moving their heads. To solve this problem, a pupil center corneal reflection (PCCR)-based method has been considered. However, the error of accurately detecting the pupil center and corneal reflection center is increased in a car environment due to various environment light changes, reflections on glasses surface, and motion and optical blurring of captured eye image. In addition, existing PCCR-based methods require initial user calibration, which is difficult to perform in a car environment. To address this issue, we propose a deep learning-based gaze detection method using a near-infrared (NIR) camera sensor considering driver head and eye movement that does not require any initial user calibration. The proposed system is evaluated on our self-constructed database as well as on open Columbia gaze dataset (CAVE-DB). The proposed method demonstrated greater accuracy than the previous gaze classification methods.
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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.