docs/user_guide/aruco_marker_pipeline/introduction.md


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Overview
========

This section explains how to build augmented reality pipelines based on [ArUco Marker technology](https://www.sciencedirect.com/science/article/abs/pii/S0031320314000235) for various use cases.  

The OpenCV library provides a module to detect fiducial markers in a picture and estimate their poses.

![OpenCV ArUco markers](../../img/opencv_aruco.png)

The ArGaze [ArUcoMarker submodule](../../argaze.md/#argaze.ArUcoMarker) eases markers creation, markers detection, and 3D scene pose estimation through a set of high-level classes.

!!! warning "Read eye tracking context and gaze analysis pipeline sections before"
	This section assumes that the incoming gaze positions are provided by an [eye tracking context](../eye_tracking_context/introduction.md) and also assumes that the way a [gaze analysis pipeline](../gaze_analysis_pipeline/introduction.md) works is understood.

First, let's look at the schema below. It gives an overview of the main notions involved in the following chapters.

![ArUco marker pipeline](../../img/aruco_marker_pipeline.png)

To build your own ArUco marker pipeline, you need to know:

* [How to setup ArUco markers into a scene](aruco_marker_description.md),
* [How to load and execute ArUco marker pipeline](configuration_and_execution.md),
* [How to estimate the scene pose](pose_estimation.md),
* [How to describe the scene's AOI](aoi_3d_description.md),
* [How to project 3D AOI into the camera frame](aoi_3d_projection.md),
* [How to define a 3D AOI as a frame](aoi_3d_frame.md).

More advanced features are also explained like:

* [How to script ArUco marker pipeline](advanced_topics/scripting.md),
* [How to calibrate optic parameters](advanced_topics/optic_parameters_calibration.md),
* [How to improve ArUco marker detection](advanced_topics/aruco_detector_configuration.md).