Real-time camera tracking using hybrid features in mobile augmented reality

Often only simple features are employed in mobile augmented reality（AR） due to the limited computational capacity of mobile terminals, which often leads to unsteadiness of the camera tracking. In this paper, we propose a novel approach to real-time camera tracking in mobile AR using hybrid features to alleviate this problem. By integrating feature points and lines as scene features, hybrid features are generated through the process of point/line features extraction, optimization and fusion, and are used in the real-time estimation of camera parameters. A method for image feature optimization is proposed based on the scene structural analysis to meet the computational constraints of mobile terminals. In order to improve the stability of camera tracking, an iterative screening method is proposed to choose a set of stable feature lines, and hybrid features are adaptively constructed based on the composition and geometry of scene features. It is shown from the experimental results that the proposed method produces more stable and smoother camera trajectories in comparison with the method only using feature points, and a good balance is achieved between the stability and the real-time computation of the camera tracking on a mobile platform.