An efficient iterative pose estimation algorithm

A novel model-based pose estimation algorithm is presented which estimates the motion of a three-dimensional object from a image sequence. The nonlinear estimation process within iteration is divided into two linear estimation stages, namely the depth approximation and the pose calculation. In the depth approximation stage, the depths of the feature points in three-dimensional space are estimated. In the pose calculation stage, the rotation and translation parameters between the estimated feature points and the model point set arer calculated by a fast singular value decomposition method. The whole process is executed recursively until the result is stable. Since both stages can be solved efficiently, the computational cost is low. As a result, the algorithm is well-suited for real computer vision applications. We demonstrate the capability of this algorithm by applying it to a real time head tracking problem. The results are satisfactory.