Model-based real-time head tracking

Abstract

This paper treats real-time tracking of a human head using an analysis by synthesis approach. The work is based on the Structure from Motion (SfM) algorithm from Azarbayejani and Pentland (1995). We will analyze the convergence properties of the SfM algorithm for planar objects, and extend it to handle new points. The extended algorithm is then used for head tracking. The system tracks feature points in the image using a texture mapped three-dimensional model of the head. The texture is updated adaptively so that points in the ear region can be tracked when the user's head is rotated far, allowing out-of-plane rotation of up to 90̈ without losing track. The covariance of the x- and the y-coordinates are estimated and forwarded to the Kaiman filter, making the tracker robust to occlusion. The system automatically detects tracking failure and reinitializes the algorithm using information gathered in the original initialization process.