Robust head tracking using 3D ellipsoidal head model in particle filter

Abstract

This paper proposes a real-time 3D head tracking method that can handle large rotation and translation. To achieve this goal, we incorporate the following three approaches into the particle filter. First, we take the 3D ellipsoidal head model to handle the large head rotation more effectively, especially the large rotation around the x-axis (pitch). Second, we take the online appearance model (OAM) that can adapt both the short-term and long-term appearance changes in the appearance model image effectively. Third, we take the adaptive state transition model to track the fast moving 3D heads, where the most plausible state for the next time is estimated by using the motion history model and the particles are distributed near the estimated state. This enables the real-time 3D head tracking by reducing the required number of particles greatly. The experimental results show that (1) the tracking accuracy of the 3D ellipsoidal head model is more precise than that of the 3D cylindrical head model by 15%, (2) the OAM provides more stable tracking than the wandering model, and (3) the adaptive state transition model can track faster moving heads than the zero-velocity model.