Tracker/Camera Calibration for Accurate Automatic Gaze Annotation of Images and Videos

Abstract

Modern appearance-based gaze tracking algorithms require vast amounts of training data, with images of a viewer annotated with “ground truth” gaze direction. The standard approach to obtain gaze annotations is to ask subjects to fixate at specific known locations, then use a head model to determine the location of “origin of gaze”. We propose using an IR gaze tracker to generate gaze annotations in natural settings that do not require the fixation of target points. This requires prior geometric calibration of the IR gaze tracker with the camera, such that the data produced by the IR tracker can be expressed in the camera’s reference frame. This contribution introduces a simple tracker/camera calibration procedure based on the PnP algorithm and demonstrates its use to obtain a full characterization of gaze direction that can be used for ground truth annotation.