Multi-distributions particle filter for eye tracking inside a vehicle

Abstract

This paper presents a new approach for driver's eye tracking, based on an improved version of a particle filter. We use two different state transition models and two different observation models distributions in order to adapt the tracking depending on the situation. The first state transition model is based on autoregressive model and is robust to face rotation. The second one is based on head motion and is efficient despite rapid head movements. The first observation model is based on pupil detection. Although very accurate, it is not extremely robust to head rotations. For that, we add a second observation model, based on similarity between eye candidates and a subspace trained offline. This approach is robust to important face rotations or partial occlusion. Evaluation has been done with an infrared camera on different people executing a challenging sequence of movements. Results show that our method is robust to face rotation, partial occlusion, and illumination variation. © 2013 Springer-Verlag.