Accurate 3D motion tracking by combining image alignment and feature matching

We presents a novel method to improve the accuracy of 3D motion tacking. In contrast to the state-of-the-art tracking approaches, where the 3D structure of target is commonly approximated by a CAD model, the proposed method establishes the target model by an online improved Structure-from-Motion technique. Furthermore, the tracking is implemented by three sequential trackers (feature-based tracker, image-alignment-based tracker and Particle Filter), which continually refine the tracking results. This coarse-to-fine method increases the accuracy of tracking. Moreover, our approach uses keyframe strategy to prevent tracking drift, the new keyframe insertion is determined by a criterion which can ensure a correct update. Thorough evaluations are performed on two public databases, the Biwi Head Pose dataset and the UPNA Head Pose Database. Comparisons illustrate that the proposed method achieves better performance with respect to other state-of-the-art tracking approaches.