docs/index.md


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37

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

# Develop post- or real-time gaze processing applications

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

**ArGaze** is a Python software library that lets you build **custom-made gaze processing pipelines** for **any kind of eye tracker device,** whether for **post- or real-time data processing**.

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

## Gaze analysis pipeline

**ArGaze** provides an 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 the incoming data is 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 the dedicated user guide section](./user_guide/gaze_analysis_pipeline/introduction.md).

## Augmented reality based on ArUco marker 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 marker pipeline can be combined with any wearable eye tracking device Python library, like Tobii or Pupil glasses.

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

!!! note
   
      *ArUco marker 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.*