docs/index.md


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---
title: What is ArGaze?
---

# Build real time or post-processing eye tracking applications

**Useful links**: [Installation](installation.md) | [Source Repository](https://git.recherche.enac.fr/projects/argaze/repository) | [Issue Tracker](https://git.recherche.enac.fr/projects/argaze/issues) | [Contact](mailto:achil-contact@recherche.enac.fr)

**ArGaze** python toolkit provides a set of classes to build **custom-made gaze processing pipelines** that works with **any kind of eye tracker devices** whether on **live data stream** or for **data post-processing**. 

![ArGaze pipeline](img/argaze_pipeline.png)

!!! warning "Eyetracker connectors are not provided"

      **ArGaze** works with any eyetracker device but there is no connector provided inside the library.
<!--
      [Read the use cases section to discover examples using specific eyetrackers](./user_cases/introduction.md).
!-->

## Gaze analysis pipeline

First of all, **ArGaze** provides extensible modules library allowing to select application specific algorithms at each pipeline step:  

* **Fixation/Saccade identification**: dispersion threshold identification, velocity threshold identification, ...  
* **Area Of Interest (AOI) matching**: focus point inside, deviation circle coverage, ...
* **Scan path analysis**: transition matrix, entropy, explore/exploit ratio, ...

Once incoming data are formatted as required, all those gaze analysis features can be used with any screen-based eye tracker devices.

[Learn how to build gaze analysis pipelines for various use cases by reading user guide dedicated section](./user_guide/gaze_analysis_pipeline/introduction.md).

## Augmented reality based on ArUco markers pipeline

Things goes harder when gaze data comes from head-mounted eye tracker devices. That's why **ArGaze** provides **Augmented Reality (AR)** support to map **Areas Of Interest (AOI)** on [OpenCV ArUco markers](https://www.sciencedirect.com/science/article/abs/pii/S0031320314000235).

![ArUco pipeline axis](img/aruco_pipeline_axis.png)

This ArUco markers pipeline can be combined with any wearable eye tracking device python library like Tobii or Pupill glasses.

[Learn how to build ArUco markers pipelines for various use cases by reading user guide dedicated section](./user_guide/aruco_markers_pipeline/introduction.md).

!!! note
   
      *ArUco markers pipeline is greatly inspired by [Andrew T. Duchowski, Vsevolod Peysakhovich and Krzysztof Krejtz article](https://git.recherche.enac.fr/attachments/download/1990/Using_Pose_Estimation_to_Map_Gaze_to_Detected_Fidu.pdf) about using pose estimation to map gaze to detected fiducial markers.*