基于空间几何结构的特征跟踪算法

针对传统的基于标记的增强现实系统场景受限等缺点,提出一种基于特征空间几何结构的无标记跟踪算法。在传统的金字塔Kanade-Lucas-Tomasi(KLT)跟踪算法基础上,通过建立图像的多尺度空间模型并在多尺度空间模型中对图像进行实时跟踪,同时根据图像特征间特有的空间几何结构信息优选跟踪特征点,解决了在多尺度变化情况下视频图像特征跟踪稳定性问题。实验结果表明,提出的跟踪算法在给定的数据库上性能高效稳定,与同类跟踪算法相比跟踪精度大幅提高,每帧重投影错误率均小于1像素,保持在亚像素级别。

Feature Tracking Based on Space Geometry Structure

In this paper, a novel method for tracking without a marker was proposed. This method alleviated many constraints in augmented reality systems based on fiducial markers. To improve the tracking stability in multi-scale, the effective features and space geometry structure were detected, and the effective features were searched in a multi-scale model. Based on space geometry structure of features, the effective feature points were selected and fictitious features points were wiped off. Experiments show that the proposed algorithm works robustly in the video with scaling objects. The feature points reprojection error of every frame is smaller than one pixel, and the average of feature points reprojection error is kept in sub-pixel.