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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Related Experiment Video

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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Model-Based 3D Gaze Estimation Using a TOF Camera.

Kuanxin Shen1, Yingshun Li2, Zhannan Guo2

  • 1School of Chemical Process Automation, Shenyang University of Technology, Liaoyang 111003, China.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary

This study introduces a novel model-based gaze-estimation algorithm using a low-resolution 3D Time-of-Flight (TOF) camera and infrared images. The method accurately estimates gaze angles, even in challenging driving environments.

Keywords:
TOF cameraYOLOv8 neural networkeye tracking on driverlow-resolution infrared imagemodel-based gaze estimation

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Appearance-based gaze estimation methods using RGB images and CNNs struggle in varying illumination.
  • Model-based methods require high-resolution images, limiting practical applications.
  • Existing techniques face challenges in outdoor and real-world scenarios.

Purpose of the Study:

  • To propose a robust model-based gaze-estimation algorithm for challenging environments.
  • To overcome illumination dependency by utilizing infrared images.
  • To enable accurate gaze estimation using low-resolution 3D Time-of-Flight (TOF) cameras.

Main Methods:

  • Utilized a trained YOLOv8 neural network for eye landmark detection.
  • Integrated depth information from a TOF camera to compute 3D canthus coordinates.
  • Developed a 3D geometric eyeball model for gaze angle determination.

Main Results:

  • Achieved a root mean square error of 6.03° (horizontal) and 4.83° (vertical) for gaze angle estimation.
  • Demonstrated stable driver gaze detection in a real car driving environment.
  • Validated performance across various in-car locations like the dashboard and mirrors.

Conclusions:

  • The proposed method offers a reliable solution for gaze estimation in diverse and challenging conditions.
  • Infrared imaging and 3D TOF data enhance robustness against illumination variations.
  • The algorithm shows significant potential for driver monitoring and human-computer interaction systems.