Computing Pose Sequences Directly from Videos

An innovative extended Kalman filter (EKF) algorithm for pose tracking has been proposed in this paper. It has the advantages of both structure and motion-based (SAM-based) and traditional model-based pose estimation algorithms. With no prior information about the scene, the pose sequence can be computed directly from images while the updating of the 3-D structure is not necessary. To achieve the goal, a constant velocity motion model is used as the dynamic system and the trifocal tensor point transfer function is applied to the measurement model of the filter. The resulting algorithm is stable, accurate and efficient. An empirical comparison with existing EKFs which deal with the same problem has been made and shows that our approach outperformed the others. The proposed method has been tested with various video sequences to demonstrate its performance in real situations.