Motion smoothness constraint for parameter optimization in real-time augmented reality

In professional video imaging, camera motion is always smooth, which is useful for the camera parameters optimization. Traditionally, optimization approaches for camera parameters focused on minimizing projection error. This paper presents a novel approach used in the real-time augmented reality software based on the Chebyshev Polynomial Fitting, using the smoothness of camera motion as an essential constraint. In order to characterize the performance of the approach, experiments with real-time video sequences are undertaken. The result analysis demonstrates the robustness of our optimization approach.