Eye Gaze Detection Dataset

Eye Gaze Detection Dataset


Eye Gaze Detection Dataset


Eye Gaze Detection Dataset

Use Case

Computer Vision


Unlock the power of eye-tracking with the UnityEyes Eye Gaze Dataset exclusively from GTS.ai. Tailored for detecting inattentive drivers.

Eye Gaze Detection Dataset

Discover the potential of the Unity Eyes Eye Gaze Dataset, designed to enhance the development of state-of-the-art eye gaze tracking technologies. This dataset is essential for creating algorithms that accurately identify inattentive drivers, increasing safety on the roads.

Dataset Overview:

The Unity Eyes Dataset offers a rich collection of eye imagery, captured through the advanced Unity Eyes simulator. Each image is meticulously tagged with the gaze direction, providing a reliable foundation for training machine learning models aimed at detecting driver inattention.

Key Features:

  • High-Quality Images: Each image in the dataset represents a variety of driving scenarios, ensuring versatility and robustness in model training.
  • Precise Gaze Labeling: Detailed annotations indicate the exact direction of the eye gaze, crucial for accurate detection of inattentiveness.

Diverse Applications: Ideal for researchers and developers in automotive safety, AI in transportation, and advanced driver-assistance systems (ADAS).

Components of the Eye Gaze Detection Dataset

Types of Data Included

The dataset includes various types of data essential for comprehensive eye-tracking research:

  • Static and Dynamic Stimuli: Data collected from experiments with both stationary images and moving objects.
  • Controlled and Natural Environments: Gaze data from both laboratory settings and real-world scenarios.
  • Diverse Participant Demographics: Data from participants of different ages, genders, and backgrounds to ensure generalizability.

High-Resolution Data

High-resolution gaze data is crucial for precision in eye-tracking applications. The dataset provides detailed measurements that capture subtle eye movements and pupil size variations.


Accompanying metadata includes information about the experimental setup, participant demographics, and conditions under which the data was collected. This context is essential for replicating studies and validating results.


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Quality Data Creation


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