Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region
  Estimation Methods in a Driving Simulator
David González-Ortega Francisco Javier Díaz-Perna Mario Martínez-Zarzuela Míriam Antón-Rodríguez
Driver's gaze information can be crucial in driving research because of its
relation to driver attention. Particularly, the inclusion of gaze data in
driving simulators broadens the scope of research studies as they can relate
drivers' gaze patterns to their features and performance. In this paper, we
present two gaze region estimation modules integrated in a driving simulator.
One uses the 3D Kinect device and another uses the virtual reality Oculus Rift
device. The modules are able to detect the region, out of seven in which the
driving scene was divided, where a driver is gazing at in every route processed
frame. Four methods were implemented and compared for gaze estimation, which
learn the relation between gaze displacement and head movement. Two are simpler
and based on points that try to capture this relation and two are based on
classifiers such as MLP and SVM. Experiments were carried out with 12 users
that drove on the same scenario twice, each one with a different visualization
display, first with a big screen and later with Oculus Rift. On the whole,
Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The
Oculus-based gaze region estimation method with the highest performance
achieved an accuracy of 97.94%. The information provided by the Oculus Rift
module enriches the driving simulator data and makes it possible a multimodal
driving performance analysis apart from the immersion and realism obtained with
the virtual reality experience provided by Oculus.