Overview
This section explains how to build augmented reality pipelines based on ArUco Marker technology for various use cases.
The OpenCV library provides a module to detect fiducial markers in a picture and estimate their poses.
The ArGaze ArUcoMarker submodule eases markers creation, markers detection, and 3D scene pose estimation through a set of high-level classes.
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 and also assumes that the way a gaze analysis pipeline works is understood.
First, let's look at the schema below. It gives an overview of the main notions involved in the following chapters.
To build your own ArUco marker pipeline, you need to know:
- How to setup ArUco markers into a scene,
- How to load and execute ArUco marker pipeline,
- How to estimate the scene pose,
- How to describe the scene's AOI,
- How to project 3D AOI into the camera frame,
- How to define a 3D AOI as a frame.
More advanced features are also explained like: