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

Abstract

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.

摘要

摘要

针对目前移动增强现实中由于计算性能限制而不得不采用简单特征描述, 导致相机运动跟踪不够稳定的问题, 提出一种基于混合特征的相机实时运动跟踪新方法, 通过综合利用特征点和特征线作为场景特征, 经特征提取、优化和融合后构造为混合特征, 并将混合特征统一用于相机参数实时预估。实验结果表明, 与仅采用特征点的方法相比, 所提出方法生成的相机运动轨迹更为稳定、平滑, 在移动平台上相机运动跟踪的稳定性和计算实时性之间取得了良好平衡。

创新点

提出基于混合特征的相机实时运动跟踪框架; 提出基于场景结构分析的图像特征优化方法; 提出基于迭代式特征线筛选的稳定特征线集构造方法。